Person Reidentification and Recognition in Video

Person recognition has been a challenging research problem for computer vision researchers for many years. A variation of this generic problem is that of identifying the reappearance of the same person in different segments to tag people in a family video. Often we are asked to answer seemingly simple queries such as ‘how many different people are in this video? or ‘find all instances of this person in these videos’. The complexity of the task grows quickly if the video in question includes segments taken at different times, places, lighting conditions, camera settings and distances since these could include substantial variations in resolution, pose, appearance, illumination, background, occlusions, etc. In some scenarios (airports, shopping centers, and city streets) we may have video feeds from multiple cameras with partially overlapping views operating under widely varying lighting and visibility conditions. Yet computer vision systems are challenged to find and track a person of interest as data from such systems have become ubiquitous and concern for security in public spaces has become a growing concern. While this is yet an unsolved challenge, much progress has been made in recent years in developing computer vision algorithms which are the building blocks for person detection, tracking and recognition. We consider several video capture scenarios, discuss the challenges they present for person re-identification and recognition as the complexity of the scene changes, and present pointers to recent research work in relevant computer vision areas in this paper.