Object Tracking and Face Recognition in Video Streams

14 Chapter 3. Implementationstrict mode, Wawo only outputs the PID if the similarity is above a given threshold; it willotherwise output unknown. The threshold is a floating-point number from 0 to 1.3.3 The reference implementationThe reference system performs two tasks: face detection and face identification. For eachframe, a set of faces is detected using OpenCV. Each face is identified using Wawo. Performingthe two tasks produces a set of face states that are reported immediately.3.4 Integrating FaceclipThe Faceclip integration was performed by replacing the relevant calls to OpenCV with thecorresponding calls to Faceclip.Using Faceclip turned out not to be feasible when training Wawo, as it found some falsefaces in the training pictures. For this reason, the old detection code is still used for theinitial training stage.3.5 Integrating object trackingIn this implementation, there are two components that may report face positions: the facedetector and the object tracker. The system also tries to identify faces detected by the facedetector, as in the reference implementation. This means that the output from the facedetector is both a position and a PID, while the output from the object tracker is just aposition.The implementation is based around face trajectories. The basic idea is that once theface detector has found a new face, the face detector and the object tracker together try tokeep track of that face in subsequent frames. Saving the face states to a single trajectoryproduces a history of states for that face.When the face has been out of picture for some frames and/or at the end of the videostream, the trajectory will be amended and reported. States are dropped from the trajectoryonce they have been reported, and will therefore have no effect on future amendmentprocesses.Each trajectory has its own object tracker. The trajectory and the corresponding trackerare used interchangably in this report. When the detector finds a face that does not have atrajectory, both a trajectory and a tracker are created.A counter C pt counts how many times there has been a detection with PID p that hasoverlapped with the tracker t. This includes when the tracker is first created. Only definedvalues of p are counted, i.e. not unknown.Since there are two components that may each find a set of face positions in a givenframe, the two sets must be combined to give a meaningful result. The procedure first findsthe set of overlapping positions, where a detection d overlaps with a tracker t. Each suchposition is added to the trajectory t, using the PID and exact coordinates from d. Each tand d can occur in at most one such overlap. The tracker positions that did not overlapwith a detection are added to their respective trajectories, using the unknown PID.The detections that did not overlap with a tracker are saved, given certain conditions.If the PID p is unknown, or if there is no C pt defined yet, a new trajectory is created, and