Object Tracking and Face Recognition in Video Streams

2 Chapter 1. Introductiona frontal picture, or it may only recognise a face in a particular “ideal”, or neutral, state.When the system fails, it can be by identifying the face as belonging to the wrong person,or it can be by failing to make an identification at all.This project aims to improve the ability to recognise a person in a non-ideal state orangle. By tracking how a face moves, the system has the potential to recognise that it isthe same face when it moves between an ideal and a non-ideal state or angle, even if Wawono longer recognises it. The system can then make a qualified guess that the identity is thesame in the two (or more) states. If Wawo makes contradictory identifications for a singleface in different frames, the identity that occurs most often can reasonably be assumed tobe the right one.The goal is to use a system called OpenTLD (Kalal, 2011b). It is an implementationof TLD, which is a set of algorithms that handle object tracking in video streams, whichbasically means that the system tracks where objects move between frames. TLD has thebonus of being adaptive, making it more flexible than the OpenCV face detector. Theoriginal OpenTLD is written in Matlab, but some C++ ports should be explored and usedinstead if possible.The integration of OpenTLD with Vidispine will be general, allowing it in theory totrack any object. This will be accomplished by writing a general plugin for object tracking.The general object-tracking plugin will then used by the face recognition plugin to trackfaces.An object can be tracked both forwards and backwards in a video stream. Tracking anobject forwards is obvious, and is the first thing that should be implemented. If the faceis only found in the middle of a scene, it may also be worth it tracking the face backwardsto see see how long it has been there. Therefore, support for tracking backwards should beexplored.A library called Faceclip (Rondahl, 2011) will be evaluated to see whether it is betterthan the current system at detecting faces in a frame, and may take over that task. Likethe current code, Faceclip is based on OpenCV. It has, however, been adjusted to do abetter job, mainly by performing a larger number of tests. Before making the decision touse Faceclip, it should be evaluated; both the results and the performance should be takeninto account to some degree.The current Wawo-based code will be used to do the actual face identification. A bonusfeature is to add support for detecting and reporting the direction of a face in a given frame.The two transcoder plugins: object tracking and face detection/identification, will becombined in a higher level face-recognition transcoder plugin.The current plugin will be used as the reference system when evaluating the recognitionresults and performance of the project.In short, the goals for this project are:G1 Create a transcoder plugin for object tracking, using OpenTLD, and integrate it withthe face recognition plugin. The first goal is to track objects forward.G2 Support tracking objects backwardsG3 Implement and evaluate Faceclip as an alternative to the current face detection code.G4 Support finding the direction of detected faces.