Framework for Pedestrian Detection, Tracking and Reidentification in Video Surveillance System In this work, we present a framework for implementing pedestrian reidentification in real world surveillance camera systems. Pedestrian reidentification has seen a lot of attention as a standalone identifier that takes in cropped images of pedestrians and matches their IDs. The focus of this work is to produce the input needed for the reidentifier network form a raw video output from the surveillance cameras. The integrated system utilizes Tensorflow object detection API with a FasterRCNN pretrained model, for pedestrian detection, a discriminative correlation filter from 1, for the purpose of shortterm tracking of subjects in each camera and a pedestrian reidentification network from 2. This configuration resulted in an accuracy above 93% on our dataset, that was automatically produced using pedestrian detection and tracking algorithms.

A Framework for RealTime FaceRecognition The advent and wide use of deeplearning technology has enabled tremendous advancements in the accuracy of face recognition under favourable conditions. Nonetheless, the reported nearperfect performance on classic benchmarks like lfw, does not include complications in unconstrained application. The research reported in this paper addresses some of the critical challenges of face recognition under adverse conditions. In this context, we introduce an endtoend framework for realtime videobased face recognition. This system detects, tracks and recognizes individuals from live video feed. The proposed system addresses three key challenges of videobased face recognition systems: endtoend computational complexity, in the wild recognition and multiperson recognition. We exploit sophisticated deep neural networks for face detection and facial feature extraction, while minimizing the computational overhead from the rest of the modules in the recognition pipeline. A comprehensive evaluation shows that the proposed system can effectively recognize faces under unconstrained conditions, at elevated frames per second rates.