An improved CNN framework for detecting and tracking human body in unconstraint environment

Abstract Human tracking and localization play a crucial role in many applications like accident avoidance, action recognition, safety and security, surveillance and crowd analysis. Inspired by its use and scope, we introduced a novel method for human tracking (one or many) and re-localization in a complex environment with large displacement. The model can handle complex background, variations in illumination, changes in target pose, the presence of similar target and appearance (pose and clothes), the motion of target and camera, occlusion of the target, background variation, and massive displacement of the target. Our model uses three convolutional neural network based deep architecture and cascades their learning such that it improves the overall efficiency of the model. The first network learns the pixel level representation of small regions. The second architecture uses these features and learns the displacement of a region with its category between moved, not-moved, and occluded classes. Whereas, the third network improves the displacement result of the second network by utilizing the previous two learning. We also create a semi-synthetic dataset for training purpose. The model is trained on this dataset first and tested on a subset of CamNeT, VOT2015, LITIV-tracking and Visual Tracker Benchmark database without training with real data. The proposed model yield comparative results with respect to current state-of-the-art methods based on evaluation criteria described in Object Tracking Benchmark, TPAMI 2015, CVPR 2013 and ICCV 2017.