Multiple pedestrian tracking under first-person perspective using deep neural network and social force optimization

Multiple pedestrian tracking in the first-person perspective is a challenging problem, obstacles of which are mainly caused by camera moving, frequent occlusions, and collision avoidance. To solve the mentioned issues, we proposed a novel deep learning-based approach. Firstly, a dense connection and attention based YOLO (DCA-YOLO) is proposed for ameliorating the detection performance. Then, the detection results are sent to a wide residual network for feature extraction. We use the Kuhn-Munkres algorithm to construct a similarity matrix and find the best match of two detection boxes. To tackle the frequent occlusion and ID-switch issues caused by collision avoidances or grouping behavior, we introduce a social force model into the proposed network to optimize the tracking results. The experimental results on widely used challenging MOT2015 and MOT2016 benchmarks demonstrate the effectiveness of our proposed algorithm.

Automated summary

The study proposes a deep learning-based approach, using DCA-YOLO, residual network, and social force model to address the multiple pedestrian tracking problem in the first-person perspective. Experimental results demonstrate the effectiveness of the algorithm.