MOTChallenge: A Benchmark for Single-Camera Multiple Target Tracking

We have compiled a total of 22 sequences that combine different videos from several sources (Andriluka et al. 2010; Benfold and Reid 2011; Ess et al. 2008; Ferryman and Ellis 2010; Geiger et al. 2012) and new data collected from us. We use half of the data for training and a half for testing, and the annotations of the testing sequences are not released to the public to avoid (over)fitting of methods to specific sequences. Note, the test data contains over 10 min of footage and 61,440 annotated bounding boxes, therefore, it is hard for researchers to over-tune their algorithms on such a large amount of data. This is one of the major strengths of the benchmark.

We collected 6 new challenging sequences, 4 filmed from a static camera and 2 from a moving camera held at pedestrian’s height. Three sequences are particularly challenging: a night sequence filmed from a moving camera and two outdoor sequences with a high density of pedestrians. The moving camera together with the low illumination creates a lot of motion blur, making this sequence extremely challenging. A smaller subset of the benchmark including only these six new sequences were presented at the 1st Workshop on Benchmarking Multi-Target Tracking,⁷ where the top-performing method reached MOTA (tracking accuracy) of only 12.7%. This confirms the difficulty of the new sequences.⁸

To detect pedestrians in all images of the MOT15 edition, we use the object detector of Dollár et al. (2014), which is based on aggregated channel features (ACF). We rely on the default parameters and the pedestrian model trained on the INRIA dataset (Dalal and Triggs 2005), rescaled with a factor of 0.6 to enable the detection of smaller pedestrians. The detector performance along with three sample frames is depicted in Fig.  2, for both the training and the test set of the benchmark. Recall does not reach 100% because of the non-maximum suppression applied.

We cannot (nor necessarily want to) prevent anyone from using a different set of detections. However, we require that this is noted as part of the tracker’s description and is also displayed in the rating table.

By the end of 2015, it was clear that a new release was due for the MOTChallenge benchmark. The main weaknesses of MOT15 were the following:To overcome these weaknesses, we created MOT16, a collection of all-new challenging sequences (including our new sequences from MOT15) and creating annotations following a more strict protocol (see Sect. C.1 of the Appendix).

We compiled a total of 14 sequences, of which we use half for training and a half for testing. The annotations of the testing sequences are not publicly available. The sequences can be classified according to moving/static camera, viewpoint, and illumination conditions (Fig.  11 in Appendix). The new data contains almost 3 times more bounding boxes for training and testing than MOT15. Most sequences are filmed in high resolution, and the mean crowd density is 3 times higher when compared to the first benchmark release. Hence, the new sequences present a more challenging benchmark than MOT15 for the tracking community.

We evaluate several state-of-the-art detectors on our benchmark, and summarize the main findings in Fig.  4. To evaluate the performance of the detectors for the task of tracking, we evaluate them using all bounding boxes considered for the tracking evaluation, including partially visible or occluded objects. Consequently, the recall and average precision (AP) is lower than the results obtained by evaluating solely on visible objects, as we do for the detection challenge.

MOT16 Detections We first train the deformable part-based model (DPM) v5 (Felzenszwalb and Huttenlocher 2006) and find that it outperforms other detectors such as Fast-RNN (Girshick 2015) and ACF (Dollár et al. 2014) for the task of detecting persons on MOT16. Hence, for that benchmark, we provide DPM detections as public detections.

MOT17 Detections For the new MOT17 release, we use Faster-RCNN (Ren et al. 2015) and a detector with scale-dependent pooling (SDP) (Yang et al. 2016), both of which outperform the previous DPM method. After a discussion held in one of the MOTChallenge workshops, we agreed to provide all three detections as public detections, effectively changing the way MOTChallenge evaluates trackers. The motivation is to challenge trackers further to be more general and work with detections of varying quality. These detectors have different characteristics, as can be seen in in Fig.  4. Hence, a tracker that can work with all three inputs is going to be inherently more robust. The evaluation for MOT17 is, therefore, set to evaluate the output of trackers on all three detection sets, averaging their performance for the final ranking. A detailed breakdown of detection bounding box statistics on individual sequences is provided in Table  10 in the Appendix.

MOTA summarizes three sources of errors with a single performance measure:where t is the frame index and GT is the number of ground-truth objects. where FN are the false negatives, i.e., the number of ground truth objects that were not detected by the method. FP are the false positives, i.e., the number of objects that were falsely detected by the method but do not exist in the ground-truth. IDSW is the number of identity switches, i.e., how many times a given trajectory changes from one ground-truth object to another. The computation of these values as well as other implementation details of the evaluation tool are detailed in Appendix Sect. D. We report the percentage MOTA \((-\infty , 100]\) in our benchmark. Note, that MOTA can also be negative in cases where the number of errors made by the tracker exceeds the number of all objects in the scene.

Justification  We note that MOTA has been criticized in the literature for not having different sources of errors properly balanced. However, to this day, MOTA is still considered to be the most expressive measure for single-camera MOT evaluation. It was widely adopted for ranking methods in more recent tracking benchmarks, such as PoseTrack (Andriluka et al. 2018), KITTI tracking (Geiger et al. 2012), and the newly released Lyft (Kesten et al. 2019), Waymo (Sun et al. 2020), and ArgoVerse (Chang et al. 2019) benchmarks. We adopt MOTA for ranking, however, we recommend taking alternative evaluation measures (Ristani et al. 2016; Wu and Nevatia 2006) into the account when assessing the tracker’s performance.

The Multiple Object Tracking Precision is the average dissimilarity between all true positives and their corresponding ground-truth targets. For bounding box overlap, this is computed as:where \(c_t\) denotes the number of matches in frame t and \(d_{t,i}\) is the bounding box overlap of target i with its assigned ground-truth object in frame t. MOTP thereby gives the average overlap of \(t_d\) between all correctly matched hypotheses and their respective objects and ranges between \(t_d:= 50\%\) and \(100\%\).

It is important to point out that MOTP is a measure of localisation precision, not to be confused with the positive predictive value or relevance in the context of precision / recall curves used, e.g., in object detection.

In practice, it quantifies the localization precision of the detector, and therefore, it provides little information about the actual performance of the tracker.

CLEAR-MOT evaluation measures provide event-based tracking assessment. In contrast, the IDF1 measure (Ristani et al. 2016) is an identity-based measure that emphasizes the track identity preservation capability over the entire sequence. In this case, the predictions-to-ground-truth mapping is established by solving a bipartite matching problem, connecting pairs with the largest temporal overlap. After the matching is established, we can compute the number of True Positive IDs (IDTP), False Negative IDs (IDFN), and False Positive IDs (IDFP), that generalise the concept of per-frame TPs, FNs and FPs to tracks. Based on these quantities, we can express the Identification Precision (IDP) as:and Identification Recall (IDR) as:Note that IDP and IDR are the fraction of computed (ground-truth) detections that are correctly identified. IDF1 is then expressed as a ratio of correctly identified detections over the average number of ground-truth and computed detections and balances identification precision and recall through their harmonic mean:

The final measures that we report on our benchmark are qualitative, and evaluate the percentage of the ground-truth trajectory that is recovered by a tracking algorithm. Each ground-truth trajectory can be consequently classified as mostly tracked (MT), partially tracked (PT), and mostly lost (ML). As defined in Wu and Nevatia (2006), a target is mostly tracked if it is successfully tracked for at least \(80\%\) of its life span, and considered lost in case it is covered for less than \(20\%\) of its total length. The remaining tracks are considered to be partially tracked. A higher number of MT and a few ML is desirable. Note, that it is irrelevant for this measure whether the ID remains the same throughout the track. We report MT and ML as a ratio of mostly tracked and mostly lost targets to the total number of ground-truth trajectories.

In certain situations, one might be interested in obtaining long, persistent tracks without trajectory gaps. To that end, the number of track fragmentations (FM) counts how many times a ground-truth trajectory is interrupted (untracked). A fragmentation event happens each time a trajectory changes its status from tracked to untracked and is resumed at a later point. Similarly to the ID switch ratio (c.f.  Sect.  D.1), we also provide the relative number of fragmentations as FM/Recall.

Before 2015, the community mainly focused on finding strong, preferably globally optimal methods to solve the data association problem. The task of linking detections into a consistent set of trajectories was often cast as, e.g., a graphical model and solved with k-shortest paths in DP\(\_\)NMS (Pirsiavash et al. 2011), as a linear program solved with the simplex algorithm in LP2D (Leal-Taixé et al. 2011), as a Conditional Random Field in DCO\(\_X\) (Milan et al. 2016), SegTrack (Milan et al. 2015), LTTSC-CRF (Le et al. 2016), and GMMCP (Dehghan et al. 2015), using joint probabilistic data association filter (JPDA) (Rezatofighi et al. 2015) or as a variational Bayesian model in OVBT (Ban et al. 2016).

A number of tracking approaches investigate the efficacy of using a Probability Hypothesis Density (PHD) filter-based tracking framework (Baisa 2019a, b; Baisa and Wallace 2019; Fu et al. 2018; Sanchez-Matilla et al. 2016; Song and Jeon 2016; Song et al. 2019; Wojke and Paulus 2016). This family of methods estimate states of multiple targets and data association simultaneously, reaching 30.72% MOTA on MOT15 (GMPHD_OGM), 41% and 40.42% on MOT16 (PHD_GSDL and GMPHD_ReId, respectively) and 49.94% (GMPHD_OGM) on MOT17.

Newer methods (Tang et al. 2015) bypassed the need to pre-process object detections with NMS. They proposed a multi-cut optimization framework, which finds the connected components in a graph that represent feasible solutions, clustering all detections that correspond to the same target. This family of methods (JMC (Tang et al. 2016), LMP (Tang et al. 2017), NLLMPA (Levinkov et al. 2017), JointMC (Keuper et al. 2018), HCC (Ma et al. 2018b)) achieve 35.65% MOTA on MOT15 (JointMC), 48.78% and 49.25% (LMP and HCC, respectively) on MOT16 and 51.16% (JointMC) on MOT17.

Motion Models  A lot of attention has also been given to motion models, used as additional association affinity cues, e.g., SMOT (Dicle et al. 2013), CEM (Milan et al. 2014), TBD (Geiger et al. 2014), ELP (McLaughlin et al. 2015) and MotiCon (Leal-Taixé et al. 2014). The pairwise costs for matching two detections were based on either simple distances or simple appearance models, such as color histograms. These methods achieve around 38% MOTA on MOT16 (see Table  2) and 25% on MOT15 (see Table  1).

Hand-Crafted Affinity Measures  After that, the attention shifted towards building robust pairwise similarity costs, mostly based on strong appearance cues or a combination of geometric and appearance cues. This shift is clearly reflected in an improvement in tracker performance and the ability for trackers to handle more complex scenarios. For example, LINF1 (Fagot-Bouquet et al. 2016) uses sparse appearance models, and oICF (Kieritz et al. 2016) use appearance models based on integral channel features. Top-performing methods of this class incorporate long-term interest point trajectories, e.g., NOMT (Choi 2015), and, more recently, learned models for sparse feature matching JMC (Tang et al. 2016) and JointMC (Keuper et al. 2018) to improve pairwise affinity measures. As can be seen in Table  1, methods incorporating sparse flow or trajectories yielded a performance boost – in particular, NOMT is a top-performing method published in 2015, achieving MOTA of 33.67% on MOT15 and 46.42% on MOT16. Interestingly, the first methods outperforming NOMT on MOT16 were published only in 2017 (AMIR (Sadeghian et al. 2017) and NLLMP (Levinkov et al. 2017)).

LP_SSVM (Wang and Fowlkes 2016) demonstrates a significant performance boost by learning the parameters of linear cost association functions within a network flow tracking framework, especially when compared to methods using a similar optimization framework but hand-crafted association cues, e.g.  Leal-Taixé et al. (2014). The parameters are learned using structured SVM (Taskar et al. 2003). MDP (Xiang et al. 2015) goes one step further and proposes to learn track management policies (birth/death/association) by modeling object tracks as Markov Decision Processes (Thrun et al. 2005). Standard MOT evaluation measures (Stiefelhagen et al. 2006) are not differentiable. Therefore, this method relies on reinforcement learning to learn these policies. As can be seen in Table 1, this method outperforms the majority of methods published in 2015 by a large margin and surpasses 30% MOTA on MOT15.

In parallel, methods start leveraging the representational power of deep learning, initially by utilizing transfer learning. MHT\(\_\)DAM (Kim et al. 2015) learns to adapt appearance models online using multi-output regularized least squares. Instead of weak appearance features, such as color histograms, they extract base features for each object detection using a pre-trained convolutional neural network. With the combination of the powerful MHT tracking framework (Reid 1979) and online-adapted features used for data association, this method surpasses MDP and attains over 32% MOTA on MOT15 and 45% MOTA on MOT16. Alternatively, JMC (Tang et al. 2016) and JointMC (Keuper et al. 2018) use a pre-learned deep matching model to improve the pairwise affinity measures. All aforementioned methods leverage pre-trained models.

Learning Appearance Models  The next clearly emerging trend goes in the direction of learning appearance models for data association in end-to-end fashion directly on the target (i.e., MOT15, MOT16, MOT17) datasets. SiameseCNN (Leal-Taixe et al. 2016) trains a siamese convolutional neural network to learn spatio-temporal embeddings based on object appearance and estimated optical flow using contrastive loss (Hadsell et al. 2006). The learned embeddings are then combined with contextual cues for robust data association. This method uses similar linear programming based optimization framework (Zhang et al. 2008) compared to LP_SSVM (Wang and Fowlkes 2016), however, it surpasses it significantly performance-wise, reaching 29% MOTA on MOT15. This demonstrates the efficacy of fine-tuning appearance models directly on the target dataset and utilizing convolutional neural networks. This approach is taken a step further with QuadMOT (Son et al. 2017), which similarly learns spatio-temporal embeddings of object detections. However, they train their siamese network using quadruplet loss (Chen et al. 2017b) and learn to place embedding vectors of temporally-adjacent detections instances closer in the embedding space. These methods reach 33.42% MOTA in MOT15 and 41.1% on MOT16.

The learning process, in this case, is supervised. Different from that, HCC (Ma et al. 2018b) learns appearance models in an unsupervised manner. To this end, they train their method using object trajectories obtained from the test set using offline correlation clustering-based tracking framework (Levinkov et al. 2017). TO (Manen et al. 2016), on the other hand, proposes to mine detection pairs over consecutive frames using single object trackers to learn affinity measures which are plugged into a network flow optimization tracking framework. Such methods have the potential to keep improving affinity models on datasets for which ground-truth labels are not available.

Online Appearance Model Adaptation  The aforementioned methods only learn general appearance embedding vectors for object detection and do not adapt the tracking target appearance models online. Further performance is gained by methods that perform such adaptation online (Chu et al. 2017; Kim et al. 2015, 2018; Zhu et al. 2018). MHT_bLSTM (Kim et al. 2018) replaces the multi-output regularized least-squares learning framework of MHT_DAM (Kim et al. 2015) with a bi-linear LSTM and adapts both the appearance model as well as the convolutional filters in an online fashion. STAM (Chu et al. 2017) and DMAN (Zhu et al. 2018) employ an ensemble of single-object trackers (SOTs) that share a convolutional backbone and learn to adapt the appearance model of the targets online during inference. They employ a spatio-temporal attention model that explicitly aims to prevent drifts in appearance models due to occlusions and interactions among the targets. Similarly, KCF (Chu et al. 2019) employs an ensemble of SOTs and updates the appearance model during tracking. To prevent drifts, they learn a tracking update policy using reinforcement learning. These methods achieve up to 38.9% MOTA on MOT15, 48.8% on MOT16 (KCF), and 50.71% on MOT17 (MHT_DAM). Surprisingly, MHT_DAM out-performs its bilinear-LSTM variant (MHT_bLSTM achieves a MOTA of 47.52%) on MOT17.

Learning to Combine Association Cues  A number of methods go beyond learning only the appearance model. Instead, these approaches learn to encode and combine heterogeneous association cues. SiameseCNN (Leal-Taixe et al. 2016) uses gradient boosting to combine learned appearance embeddings with contextual features. AMIR (Sadeghian et al. 2017) leverages recurrent neural networks in order to encode appearance, motion, pedestrian interactions and learns to combine these sources of information. STRN (Xu et al. 2019) proposes to leverage relational neural networks to learn to combine association cues, such as appearance, motion, and geometry. RAR (Fang et al. 2018) proposes recurrent auto-regressive networks for learning a generative appearance and motion model for data association. These methods achieve 37.57% MOTA on MOT15 and 47.17% on MOT16.

Fine-Grained Detection  A number of methods employ additional fine-grained detectors and incorporate their outputs into affinity measures, e.g., a head detector in the case of FWT (Henschel et al. 2018), or a body joint detectors in JBNOT (Henschel et al. 2019), which are shown to help significantly with occlusions. The latter attains 52.63% MOTA on MOT17, which places it as the second-highest scoring method published in 2019.

Tracking-by-Regression  Several methods leverage ensembles of (trainable) single-object trackers (SOTs), used to regress tracking targets from the detected objects, utilized in combination with simple track management (birth/death) strategies. We refer to this family of models as MOT-by-SOT or tracking-by-regression. We note that this paradigm for MOT departs from the traditional view of the multi-object tracking problem in computer vision as a generalized assignment problem (or multi-dimensional assignment problem), i.e. the problem of grouping object detections into a discrete set of tracks. Instead, methods based on target regression bring the focus back to the target state estimation. We believe the reasons for the success of these methods is two-fold: (i) rapid progress in learning-based SOT (Held et al. 2016; Li et al. 2018) that effectively leverages convolutional neural networks, and (ii) these methods can effectively utilize image evidence that is not covered by the given detection bounding boxes. Perhaps surprisingly, the most successful tracking-by-regression method, Tracktor (Bergmann et al. 2019), does not perform online appearance model updates (c.f., STAM, DMAN (Chu et al. 2017; Zhu et al. 2018) and KCF (Chu et al. 2019)). Instead, it simply re-purposes the regression head of the Faster R-CNN (Ren et al. 2015) detector, which is interpreted as the target regressor. This approach is most effective when combined with a motion compensation module and a learned re-identification module, attaining 46% MOTA on MOT15 and 56% on MOT16 and MOT17, outperforming methods published in 2019 by a large margin.

Towards End-to-End Learning  Even though tracking-by-regression methods brought substantial improvements, they are not able to cope with larger occlusions gaps. To combine the power of graph-based optimization methods with learning, MPNTrack (Brasó and Leal-Taixé 2020) proposes a method that leverages message-passing networks (Battaglia et al. 2016) to directly learn to perform data association via edge classification. By combining the regression capabilities of Tracktor (Bergmann et al. 2019) with a learned discrete neural solver, MPNTrack establishes a new state of the art, effectively using the best of both worlds—target regression and discrete data association. This method is the first one to surpass MOTA above 50% on MOT15. On the MOT16 and MOT17 it attains a MOTA of 58.56% and 58.85%, respectively. Nonetheless, this method is still not fully end-to-end trained, as it requires a projection step from the solution given by the graph neural network to the set of feasible solutions according to the network flow formulation and constraints.

Alternatively, (Xiang et al. 2020) uses MHT framework (Reid 1979) to link tracklets, while iteratively re-evaluating appearance/motion models based on progressively merged tracklets. This approach is one of the top on MOT17, achieving 54.87% MOTA.

In the spirit of combining optimization-based methods with learning, Zhang et al. (2020) revisits CRF-based tracking models and learns unary and pairwise potential functions in an end-to-end manner. On MOT16, this method attains MOTA of 50.31%.

We do observe trends towards learning to perform end-to-end MOT. To the best of our knowledge, the first method attempting this is RNN_LSTM (Milan et al. 2017), which jointly learns motion affinity costs and to perform bi-partite detection association using recurrent neural networks (RNNs). FAMNet (Chu and Ling 2019) uses a single network to extract appearance features from images, learns association affinities, and estimates multi-dimensional assignments of detections into object tracks. The multi-dimensional assignment is performed via a differentiable network layer that computes rank-1 estimation of the assignment tensor, which allows for back-propagation of the gradient. They perform learning with respect to binary cross-entropy loss between predicted assignments and ground-truth.

All aforementioned methods have one thing in common—they optimize network parameters with respect to proxy losses that do not directly reflect tracking quality, most commonly measured by the CLEAR-MOT evaluation measures (Stiefelhagen et al. 2006). To evaluate MOTA, the assignment between track predictions and ground truth needs to be established; this is usually performed using the Hungarian algorithm (Kuhn and Yaw 1955), which contains non-differentiable operations. To address this discrepancy DeepMOT (Xu et al. 2020) proposes the missing link—a differentiable matching layer that allows expressing a soft, differentiable variant of MOTA and MOTP.

Conclusion  In summary, we observed that after an initial focus on developing algorithms for discrete data association (Dehghan et al. 2015; Le et al. 2016; Pirsiavash et al. 2011; Zhang et al. 2008), the focus shifted towards hand-crafting powerful affinity measures (Choi 2015; Kieritz et al. 2016; Leal-Taixé et al. 2014), followed by large improvements brought by learning powerful affinity models (Leal-Taixe et al. 2016; Son et al. 2017; Wang and Fowlkes 2016; Xiang et al. 2015).

In general, the major outstanding trends we observe in the past years all leverage the representational power of deep learning for learning association affinities, learning to adapt appearance models online (Chu et al. 2019, 2017; Kim et al. 2018; Zhu et al. 2018) and learning to regress tracking targets (Bergmann et al. 2019; Chu et al. 2019, 2017; Zhu et al. 2018). Figure 6 visualizes the promise of deep learning for tracking by plotting the performance of submitted models over time and by type.

The main common components of top-performing methods are: (i) learned single-target regressors (single-object trackers), such as (Held et al. 2016; Li et al. 2018), and (ii) re-identification modules (Bergmann et al. 2019). These methods fall short in bridging large occlusion gaps. To this end, we identified Graph Neural Network-based methods (Brasó and Leal-Taixé 2020) as a promising direction for future research. We observed the emergence of methods attempting to learn to track objects in end-to-end fashion instead of training individual modules of tracking pipelines (Chu and Ling 2019; Milan et al. 2017; Xu et al. 2020). We believe this is one of the key aspects to be addressed to further improve performance and expect to see more approaches leveraging deep learning for that purpose.

Different methods require a varying amount of computational resources to track multiple targets. Some methods may require large amounts of memory while others need to be executed on a GPU. For our purpose, we ask each benchmark participant to provide the number of seconds required to produce the results on the entire dataset, regardless of the computational resources used. It is important to note that the resulting numbers are therefore only indicative of each approach and are not immediately comparable to one another.

Figure 7 shows the relationship between each submission’s performance measured by MOTA and its efficiency in terms of frames per second, averaged over the entire dataset. There are two observations worth pointing out. First, the majority of methods are still far below real-time performance, which is assumed at 25 Hz. Second, the average processing rate \(\sim 5\) Hz does not differ much between the different sequences, which suggests that the different object densities (9 ped./fr. in MOT15 and 26 ped./fr. in MOT16/MOT17) do not have a large impact on the speed the models. One explanation is that novel learning methods have an efficient forward computation, which does not vary much depending on the number of objects. This is in clear contrast to classic methods that relied on solving complex optimization problems at inference, which increased computation significantly as the pedestrian density increased. However, this conclusion has to be taken with caution because the runtimes are reported by the users on a trust base and cannot be verified by us.

As we now, different applications have different requirements, e.g., for surveillance it is critical to have few false negatives, while for behavior analysis, having a false positive can mean computing wrong motion statistics. In this section, we take a closer look at the most common errors made by the tracking approaches. This simple analysis can guide researchers in choosing the best method for their task. In Fig.  8, we show the number of false negatives (FN, blue) and false positives (FP, red) created by the trackers on average with respect to the number of FN/FP of the object detector, used as an input. A ratio below 1 indicates that the trackers have improved in terms of FN/FP over the detector. We show the performance of the top 15 trackers, averaged over sequences. We order them according to MOTA from left to right in decreasing order.

We observe all top-performing trackers reduce the amount of FPs and FNs compared to the public detections. While the trackers reduce FPs significantly, FNs are decreased only slightly. Moreover, we can see a direct correlation between the FN and tracker performance, especially for MOT16 and MOT17 datasets, since the number of FNs is much larger than the number of FPs. The question is then, why are methods not focusing on reducing FNs? It turns out that “filling the gaps“ between detections, what is commonly thought trackers should do, is not an easy task.

It is not until 2018 that we see methods drastically decreasing the number of FNs, and as a consequence, MOTA performance leaps forward. As shown in Fig.  6, this is due to the appearance of learning-based tracking-by-regression methods (Bergmann et al. 2019; Brasó and Leal-Taixé 2020; Chu et al. 2017; Zhu et al. 2018). Such methods decrease the number of FNs the most by effectively using image evidence not covered by detection bounding boxes and regressing targets to areas where they are visible but missed by detectors. This brings us back to the common wisdom that trackers should be good at “filling the gaps“ between detections.