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Object tracking, by definition, is to track an object (or multiple objects) over a sequence of images. Object tracking, in general, is a challenging problem. Difficulties in tracking objects can arise due to abrupt object motion, changing appearance patterns of both the object and the scene, non-rigid object structures, object-to-object and object-to-scene occlusions, and camera motion. Tracking is usually performed in the context of higher-level applications that require the location and/or shape of the object in every frame. Generally there are three types of tracking algorithm:
In our research, we studied feature-based point tracking and meanshift kernel tracking.
We implemented these two algorithms to a MATLAB graphical user interface. The program loads a set of qcif video sequences as input and displays the tracking result. User can set arbitrary frame as beginning or ending frame by previewing the frames before tracking. Two output file types are supported: avi video clip and animated gif image. Demo1: Meanshift tracking: (Please wait while media is loading and please make sure your Explorer does not block activeX contents) If you cannot see the demos, you can also download them here: Meanshift tracking demo and Feature tracking demo To achieve best visual quality, please set your screen resolution higher than 1280*1024.
Demo2: Feature-based tracking:
Matlab source code: TrackingGui.zip Test video sequences can be find at: http://media.xiph.org/video/derf/
Based on the tracking test bench, we develop an objective tracking assessment algorithm. The assessment algorithm is focused on two major criteria: (1) If the tracking algorithm track the target object. (T/F problem)
(2) How good the tracking algorithm works. (Accuracy problem)
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