Real-time high-precision pedestrian tracking: a detection–tracking–correction strategy based on improved SSD and Cascade R-CNN

The existing pedestrian tracking applications are challenging to balance real-time performance and accuracy. We propose a detection–tracking–correction strategy based on the improved single-shot multi-box detector (SSD), Deep-SORT, and the improved multi-stage object detection architecture (Cascade-R-CNN), which takes both real-time performance and accuracy into consideration. For the detection mechanism, the SSD network is fast and efficient, but the disadvantage of the SSD network is relatively low accuracy. Therefore, the tricks such as cross-entropy loss function, deconvolution, and non-maximum suppression are introduced to improve the SSD network. Then, the improved SSD network is used as the central pedestrian detector to ensure real-time performance. For the tracking mechanism, the Deep-SORT is used to improve the mismatch between tracking and detection. For the correction mechanism, the improved Cascade R-CNN (introducing deformable convolution and group normalization) is used as the reference network to correct the detection errors. The experiment on the data set OTB-100 shows that the proposed strategy has good stability and adaptability in various complex scenes, and the conditions of missed detection and false detection are significantly reduced.