Multi-Object Recognition and Tracking with Automated Image Annotation for Big Data Based Video Surveillance

摘要

Presently, the scope and application of Big Data Analytics in video surveillance makes it possible in different domains. In the area of intelligent visual surveillance, the procedure of tracking is described as finding a path or trajectory of an object of a given video sequence. Multi-Object tracking (MOT) mechanism become more familiar because of its applicability in numerous ways. Generally, MOT is employed to predict the position of various specified objects across multiple consequent frames with the offered ground truth position of the target in the beginning frame. In this paper, we have introduced an improved region based scalable convolution neural network (IRS-CNN) based MOT model. The presented IRS-CNN model enhances the existing RS-CNN by incorporating an automated image annotation (AIA) tool for increasing the detection rate as well as reducing the computation time. The interesting feature of AIA tool helps to rapidly annotate the training images in an automatic way. The novel IRS-CNN approach is tested against a benchmark UCSD anomaly detection dataset. A broad experimental result verified the optimal behavior of IRS-CNN model against a set of applied test images over the compared methods.