Spatio-Temporal Processing for Automatic Vehicle Detection in Wide-Area Aerial Video

TL;DR

This paper introduces a spatio-temporal processing scheme that enhances vehicle detection accuracy in aerial videos by replacing simple thresholding with multi-neighborhood hysteresis and applying morphological and temporal post-processing, significantly reducing false positives.

Contribution

The paper proposes a novel spatio-temporal processing framework that improves existing vehicle detection algorithms in aerial videos through multi-neighborhood hysteresis thresholding and combined spatial-temporal post-processing.

Findings

Average F-score exceeds 0.8 with the scheme.

False positives reduced by 83.8% on average.

Improves performance across nine detection algorithms.

Abstract

Vehicle detection in aerial videos often requires post-processing to eliminate false detections. This paper presents a spatio-temporal processing scheme to improve automatic vehicle detection performance by replacing the thresholding step of existing detection algorithms with multi-neighborhood hysteresis thresholding for foreground pixel classification. The proposed scheme also performs spatial post-processing, which includes morphological opening and closing to shape and prune the detected objects, and temporal post-processing to further reduce false detections. We evaluate the performance of the proposed spatial processing on two local aerial video datasets and one parking vehicle dataset, and the performance of the proposed spatio-temporal processing scheme on five local aerial video datasets and one public dataset. Experimental evaluation shows that the proposed schemes improve vehicle detection performance for each of the nine algorithms when evaluated on seven datasets. Overall, the use of the proposed spatio-temporal processing scheme improves average F-score to above 0.8 and achieves an average reduction of 83.8% in false positives.