Pedestrian Tracking by using Deep Neural Networks
Abstract: This project aims at using deep learning to solve the pedestrian tracking problem for Autonomous driving usage. The research area is in the domain of computer vision and deep learning. Multi-Object Tracking (MOT) aims at tracking multiple targets simultaneously in a video data. The main application scenarios of MOT are security monitoring and autonomous driving. In these scenarios, we often need to track many targets at the same time which is not possible with only object detection or single object tracking algorithms for their lack of stability and usability. Therefore we need to explore the area of multiple object tracking. The proposed method breaks the MOT into different stages and utilizes the motion and appearance information of targets to track them in the video data. We used three different object detectors to detect the pedestrians in frames, a person re-identification model as appearance feature extractor and Kalman filter as motion predictor. Our proposed model achieves 47.6% MOT accuracy and 53.2% in IDF1 score while the results obtained by the model without person re-identification module is only 44.8% and 45.8% respectively. Our experiment results indicate the fact that a robust multiple object tracking algorithm can be achieved by splitted tasks and improved by the representative DNN based appearance features.
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