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Long-range target detection in a cluttered environment using multisensor image sequences

An approach to the long range automatic detection of vehicles, using multi-sensor image sequences, is presented. The algorithm was tested on a database of six sequences, acquired under diverse operational conditions. The vehicles in the sequences can be either moving or stationary. The sensors also can be moving. The presented approach consists of two parts. The first part detects targets in single images using seven texture measurements. The values of some of the textural features at a target position will differ from those found in the background. To perform a first classification between target- and non-target pixels, linear discriminant analysis is used on one test image for each type of sensor. Because the features are closely linked to the physical properties of the sensors, the discriminant function also gives good results to the remainder of the database sequences. By applying the discriminant function to the feature space of textural parameters, a new image is created. The local maxima of this image correspond to probably target positions. To reduce the false alarm rate, any available prior knowledge about possible target size and aspect ratio is incorporated using a region growing procedure around the local maxima. The second part of the algorithm detects moving targets. First any motion of the sensor itself need to be detected. The detection is based on a comparison of the spatial cooccurrence matrix within one image and the temporal cooccurrence matrix between successive images. If sensor motion is detected, it is estimated using a multi-resolution Markov Random Field model. Available prior knowledge about the sensor motion is used to simplify the motion estimation. The motion estimate is used to warp past images onto the current one. Moving targets are detected by thresholding the difference between the original and warped images. Temporal and spatial consistency are used to reduce false alarm rate.