OBJECT DETECTIONTracking object requires prior knowledge of the shape and appearance of the objects of interest. For this purpose a model of the object is needed. Several approaches exist for extraction of an initial model for tracking, Kim and Hwang (2002) presented a method for detecting moving objects in a video sequence, their work employs the edge map difference between consecutive frames to extract moving objects. The model is called a segmentation of the moving object from the sequence, it is then tracked in subsequent frames. To simplify and constrain the problem of obtaining an initial model, few assumptions were made: These are that the background is stationary, there is a single moving object, the camera is stationary and there is no occlusion.
Initial segmentation:Initial segmentation is carried out to detect probable moving targets in video sequences. The underlying principle is to take the absolute difference of two consecutive frames. An optimum threshold function is then used to determine the change. If In(u,v) is the intensity of the (u,v) pixel in the n frame, then the motion region Mn(u,v) can be extracted by th th obtaining the difference image of In with In-1 and then thresholding for obtained a binary masks. Pixels of motion are grouped into blobs, regions of connected pixels. The threshold is not yet automated but input by the user. To avoid false motion indication each individual frame is filtered to remove noise in the difference image by using filters resulting from mathematical morphology which have the property to preserve contours of the image. This method is very sensitive to changes in light or movement of objects in the background but it is faster than the other methods and gives a good result when one moving object must be tracked.
Extraction of an initial model for tracking:The primary aim of object extraction is to locate and delineate the area of an interesting object in a given image. In our case, the target object is extracted using the thresholding difference between two edge maps obtained by SNN, the first representing the background image and the second an image of sequence as shown in Fig. 3a-d.In this approach, the object model, which is usually in the form of edge map detected by the spiking neural network SNN is continuously updated in each frame of the sequence after the object was detected; i.e., the target model becomes the reference model. This update is recommended to consider only small movements of a non-rigid object between two consecutive frames. The core of this technique is an object tracker that maintains the temporal correspondence of objects throughout the video sequence.
THE HAUSDORFF DISTANCE MEASURESHausdorff distance is a scalar measure of the distance between two sets of points. In practice, the two sets of points may be obtained by edge detecting a reference image and a target image to determine the current position of the selected object within the image.
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