Object Tracking in Crowd Environment Using Deep Learning

Abstract

Abstract: Object tracking is fetching a primary set of object identification, allocating a unique ID to each one of them, and then ensuing each of the objects as they move around frames in a movie while safe keeping the ID assignment. Person tracking is a difficult task in video surveillance. In recent years, many computer vision, deep and machine learning have been developed. Convolutional Neural Networks (CNN) are transforming target tracking. The project's purpose is to recognize and track images utilizing object identification techniques such as Region based Convolutional Neural Networks (RCNN), Faster RCNN, Single Shot Detector (SSD), and You Only Look Once (YOLO). Faster-RCNN and SSD have superior accuracy among them, whereas YOLO performs better when speed is prioritized above accuracy. Deep learning combines SSD and Mobile Nets for efficient identification and tracking implementation. This technique detects objects quickly and efficiently without sacrificing performance. Convolutionally default boxex are passed over several feature maps. If a detected object matches one of the object classifiers during prediction, a score is produced..