Object detection is crucial for many research areas in computer vision, image analysis and pattern recognition. Since vehicles in wide-area images appear with variable shape and size, illumination changes, partial occlusion, and background clutter, automatic detection has often been a challenging task. We present a brief study of various techniques for object detection and image segmentation, and contribute to a variety of algorithms for detecting vehicles in traffic lanes from two low-resolution aerial video datasets. We present twelve detection algorithms adapted from previously published work, and we propose two post-processing schemes in contrast to four existing schemes to reduce false detections. We present the results of several experiments for quantitative evaluation by combining detection algorithms before and after using a post-processing scheme. Manual segmentation of each vehicle in the cropped frames serves as the ground truth. We classify several types of detections by comparing the binary detection output to the ground truth in each frame, and use two sets of evaluation metrics to measure the performance. A pixel classification scheme is also derived for spatial post-processing applied to seven detection algorithms, among which two algorithms are selected for sensitivity analysis with respect to a range of overlap ratios. Six tracking algorithms are selected for performance analysis for overall accuracy under four different scenarios for sample frames in Tucson dataset.
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