Comprehensive Analysis of Object Detection And Tracking Methodologies From Surveillance Videos

With an exponential advancement in technology, it's not uncommon that there is a rise in cameras and surveillance systems over the last few decades. As the world moves towards automated era monitoring and surveillance tasks dependency with humans is decreasing. On the contrary to human capabilities which are susceptible to errors and inconsistency, automated systems seem to be more promising and convenient and hence paves a way for a research trend comprising object detection and tracking in computer vision. Object detection and tracking in surveillance videos refers to accurately identifying the relative position/orientation of an object of interest and its trajectory of motion across the video sequence. Though having access to very high definition robust surveillance systems, practical environmental conditions such as occlusion, motion blur, cluttered background, illumination changes poses significant challenges in object detection and tracking. The intent of this research is to shed a light on recent innovations and developments that have enabled tackling the challenges to some extent. This research encloses brief understanding of recent object detection and tracking methodologies that are gaining popularity. In this paper, conventional as well as soft computing methodologies are covered. This paper aims to provide tentative guidance to young researchers to quickly grasp the field and can help enlighten improvements and innovation in existing methodologies.