Novelty detection in human tracking based on spatiotemporal oriented energies

Abstract

Integrated analysis of spatial and temporal domains are considered to overcome some of the challenging computer vision problems such as 'Dynamic Scene Understanding' and 'Action Recognition'. In visual tracking, 'Spatiotemporal Oriented Energy' (SOE) features are successfully applied to locate the object in cluttered scenes under varying illumination. In contrast to previous studies, this paper introduces SOE features for occlusion modeling and novelty detection in tracking. To this end, we propose a Bayesian state machine that exploits SOE information to analyze occlusion and identify the target status in the course of tracking. The proposed approach can be seamlessly merged with a generic tracking system to prevent template corruption (for example when the target is occluded). Comparative evaluations show that the proposed approach could significantly improve the performance of a generic tracking system in challenging occlusion situations.

Vision-Based Novelty Detection Using Deep Features and Evolved Novelty Filters for Specific Robotic Exploration and Inspection Tasks

“…This is a challenging problem because the datasets may have a large number of examples of the normal class and an insufficient number of examples of the novel class (in almost all cases, no novelty examples are available). Having robust methods for this type of problem is of great importance in practical applications such as fraud detection [2,3], fault detection [4], medical diagnosis [5,6,7], video surveillance [8,9], and robotic tasks [10,11,12], among others. For these applications, it is not common to have access to data labeled as novel.…”