Human Detection, Tracking and Segmentation in Surveillance Video
Explore human detection, tracking, and segmentation techniques in surveillance video through this doctoral dissertation defense. Delve into scene-specific learning approaches, including DPM human detectors, superpixel-based Bag-of-Words classifiers, and part-based person-specific SVM models. Discover methods for handling occlusions in detection and tracking, as well as separating human and background superpixels using Conditional Random Fields. Learn about leveraging spatio-temporal constraints with tracklet-based Gaussian Mixture Models and multi-frame graph optimization. Examine the development of NONA, an efficient real-time tracking system for high-definition surveillance video, implemented using Intel Threading Building Blocks. Gain insights into Fast Fourier Transform-based normalized cross-correlation, Adaptive Template scaling, and Local Frame Differencing techniques for improved tracking performance.