Tracking Pedestrian with Multi-component Online Deformable Part-Based Model

In this work we present a novel online algorithm to track pedestrian by integrating both the bottom-up and the top-down models of pedestrian. Motivated by the observation that the appearance of a pedestrian changes a lot in different perspectives or poses, the proposed bottom-up model has multiple components to represent distinct groups of the pedestrian appearances. Also, similar pedestrian appearances have several common salient local patterns and their structure is relatively stable. So, each component of the proposed bottom-up model uses an online deformable part-based model (OLDPM) containing one root and several shared parts to represent the flexible structure and salient local patterns of an appearance. We term the bottom-up model multi-component OLDPM in this paper. We borrow an offline trained class specific pedestrian model [19] as the top-down model. The top-down model is used to extend the bottom-up model with a new OLDPM when a new appearance can’t be covered by the bottom-up model. The multi-component OLDPM has three advantages compared with other models. First, through an incremental support vector machine (INCSVM) [2] associated with the each component, the OLDPM of each component can effectively adapt to the pedestrian appearance variations of a specified perspective and pose. Second, OLDPM can efficiently generate match penalty maps of parts preserving the 2bit binary pattern (2bitBP) [10] through robust real-time pattern matching algorithm [16], and can search over all possible configurations in an image in linear-time by distance transforms algorithm [5]. Last but not least, parts can be shared among components to reduce the computational complexity for matching. We compare our method with four cutting edge tracking algorithms over seven visual sequences and provide quantitative and qualitative performance comparisons.