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Tracking multiple-sized objects in low resolution and noisy imagesAbstract - In recent years, we see an increase of interest for efficient tracking systems in surveillance applications. Many of the proposed techniques work well for good quality images and when objects are within a certain size. When dealing with UAV or surveillance cameras, the images are noisy and many techniques fail to detect and track the real moving objects. This work presents a tracking technique based on a combined spatial and temporal wavelet processing of the image sequence. For sequences coming from an UAV, images are rectified using detected features in the scene. A modified Harris corner detector is used to select points of interest. Regions around these points are matched in successive frames in order to find the transformations between successive images. These transformations are used to stabilize the images and to build a complete scene mosaic from the original sequence during the object tracking. A spatial discrete wavelet transform is then used to extract potential target regions. These detections are refined using a temporal wavelet transform. Mathematical morphology is then used to eliminate targets resulting from image noise. The remaining targets are further processed using Kalman filter. A refinement selection strategy is then performed to keep only the targets obtaining the highest scores. The obtained results are promising and show the possibility of efficiently tracking moving objects in noisy images captured by a moving camera. Also, the proposed technique works efficiently with noisy infrared sequences captured by a surveillance system.  Bibtex:
@inproceedings{Akhloufi796, Last modification: 2010/02/21 by akhloufi |
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