An Efficient Unusual Event Tracking in Video Sequence using Block Shift Feature Algorithm

Abstract

The area of video technology is rapidly growing owing to advancements in intelligent video systems in sensor operations, higher bandwidth capacity, storage, and highresolution displays. This led to the proliferation of video-based computing modeling to perform specific tasks on video sequences to gain more insight from the data. Visual tracking of events is a core component in video visual surveillance systems that classify and track moving objects to describe their behavioral aspects. The prime motive behind intelligent video systems is to perform efficient video analytics to meet the specific requirements of the user/use-cases. It involves a self-directed paradigm to understand event sequences, reducing the computational burden of characterizing the activities. The study incorporates a block-shift feature algorithm and introduces a novel computational research method for unusual event tracking in video sequences. The formulated approach employs a framework combining operational blocks to compute sequential operations such as block-matching from the dictionary of motion estimations. Before applying the learning model, the subsequent analysis procedure adds feature lexicon and dominant attributes to make the execution computationally efficient. Further, it uses a sparsenon negative factorization approach to organize the informative details into k possible finite clusters. The event detection outcome from the training datasets of video sequences shows better experimental results than the traditional highly cited related approach of unusual object detection and tracking.