Chapters authored
Pedestrian Detection and Tracking in Video Surveillance System: Issues, Comprehensive Review, and Challenges
Pedestrian detection and monitoring in a surveillance system are critical for numerous utility areas which encompass unusual event detection, human gait, congestion or crowded vicinity evaluation, gender classification, fall detection in elderly humans, etc. Researchers’ primary focus is to develop surveillance system that can work in a dynamic environment, but there are major issues and challenges involved in designing such systems. These challenges occur at three different levels of pedestrian detection, viz. video acquisition, human detection, and its tracking. The challenges in acquiring video are, viz. illumination variation, abrupt motion, complex background, shadows, object deformation, etc. Human detection and tracking challenges are varied poses, occlusion, crowd density area tracking, etc. These results in lower recognition rate. A brief summary of surveillance system along with comparisons of pedestrian detection and tracking technique in video surveillance is presented in this chapter. The publicly available pedestrian benchmark databases as well as the future research directions on pedestrian detection have also been discussed.
Part of the book: Recent Trends in Computational Intelligence
Deep Learning Approach to Key Frame Detection in Human Action Videos
A key frame is a representative frame which includes the whole facts of the video collection. It is used for indexing, classification, evaluation, and retrieval of video. The existing algorithms generate relevant key frames, but additionally, they generate a few redundant key frames. A number of them are not capable of constituting the entire shot. In this chapter, an effective algorithm primarily based on the fusion of deep features and histogram has been proposed to overcome these issues. It extracts the maximum relevant key frames by way of eliminating the vagueness of the choice of key frames. It can be applied parallel and concurrently to the video sequence, which results in the reduction of computational and time complexity. The performance of this algorithm indicates its effectiveness in terms of relevant key frame extraction from videos.
Part of the book: Recent Trends in Computational Intelligence