Journal ArticleDOI
RETRACTED ARTICLE: Moving object surveillance using object proposals and background prior prediction
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A moving object detection algorithm is combined with a background estimate and a Bing (Binary Norm Gradient) object is proposed in video surveillance and can not only achieve high detection rate (DR), but also reduce false alarm rate (FAR) and time cost.Abstract:
In this paper, a moving object detection algorithm is combined with a background estimate and a Bing (Binary Norm Gradient) object is proposed in video surveillance. A simple background estimation method is used to detect rough images of a group of moving foreground objects. The foreground setting in the foreground will estimate another set of candidate object windows, and the target (pedestrian / vehicle) from the intersection area comes from the first two steps. In addition, the time cost is reduced by the estimated area. Experiments on outdoor datasets show that the proposed method can not only achieve high detection rate (DR), but also reduce false alarm rate (FAR) and time cost.read more
Citations
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Efficient vehicle detection and tracking strategy in aerial videos by employing morphological operations and feature points motion analysis
TL;DR: An efficient and robust real-time approach for automatic vehicle detection and tracking in aerial videos that employ both detections and tracking features to enhance the final decision and achieves a fast processing speed.
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A High-Performance Object Proposals based on Horizontal High Frequency Signal
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BIHL:A Fast and High Performance Object Proposals based on Binarized HL Frequency
TL;DR: This work proposes a class-independent object proposal algorithm BIHL, which combines the advantages of window scoring and superpixel merging and is the method with the highest average repeatability among the methods that achieve good repeatability to various disturbances, and theaverage repeatability is 10% higher than RPN.
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