Abstract
In this paper, we propose a method for calculating the dynamic background region in a video and removing false positives in order to overcome the problems of false positives that occur due to the dynamic background and frame drop at slow speeds. Therefore, we need an efficient algorithm with a robust performance value including processing speed. The foreground is separated from the background by comparing the similarities between false positives and the foreground. In order to improve the processing speed, the median filter was optimized for the binary image. The proposed method was based on a CDnet 2012/2014 dataset and we achieved precision of 76.68%, FPR of 0.90%, FNR of 18.02%, and an F-measure of 75.35%. The average ranking across categories is 14.36, which is superior to the background subtraction method. The proposed method was operated at 45 fps (CPU), 150 fps (GPU) at 320 × 240 resolution. Therefore, we expect that the proposed method can be applied to current commercialized CCTV without any hardware upgrades.
Highlights
Motion detection algorithms that can be applied to surveillance cameras, such as Closed-CircuitTelevision (CCTV), have been studied extensively and should have robust performance results; processing speed is an important factor
The purpose of this paper is to propose an algorithm with robust performance and fast processing speed
When the foreground is detected in the dynamic background region, it is removed by re-checking for false positives in the dynamic background samples
Summary
Motion detection algorithms that can be applied to surveillance cameras, such as Closed-CircuitTelevision (CCTV), have been studied extensively and should have robust performance results; processing speed is an important factor. If the processing speed is slow, the performance degradation can occur due to frame drop. The performance of the motion detection algorithm that uses the information of the previous frame is degraded. Another important issue in motion detection algorithms is the elimination of false positives in dynamic backgrounds, such as leaves, rivers, and so on. We propose a method for calculating the dynamic background region in a video and removing false positives in order to overcome the problems of false positives that occur due to the dynamic background and frame drop at slow speeds. Research has shown that using the LBSP [19] feature in the background subtraction algorithm achieves good performance [20].
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