Tracking of Load Handling Forklift Trucks and of Pedestrians in Warehouses

Trajectory computation for forklifts and pedestrians is of relevance for warehousing applications such as pedestrian safety and process optimization. We recorded a novel dataset with a varying range of forklift models and pedestrians, busy with loading or unloading in warehouses. We have videos with frequently occluded trucks in aisles and besides racks, some with busy pedestrian activity, such as in docking areas. Robust target localisation is very essential for seamless tracking results. For localising forklift trucks/pedestrians, we trained a deep-learning based, faster region-based convolution neural network (faster RCNN) on our own recorded data. We used detection from the model output to configure a Kalman filter to estimate the trajectories in the image plane. We also improved the forklift trajectory based on computing pixel saliency maps for the region of interest detected by faster RCNN. Our analysis shows that with robust target detection (fewer false positives and false negatives) from our trained network and Kalman-filter-based state correction, tracking results are close to ground truth.