Counting people entering and exiting a store help boost in-store analytics & facilitate marketing segmentation. This is a problem of the detection and tracking people from surveillance videos. In order to solve the problem of detecting people in each video frame, Deep Learning approach is used. In details, a detection engine is built by making uses of TensorFlow’s Object detection API/ Faster R-CNN. After recognizing people and PeopleID is generated, SORT/ deep SORT, a tracking algorithm for 2D multiple object tracking in video sequences, is applied for real-time tracking people. The project is now going to evaluation phase.
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