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- W2460996549 abstract "Automated crowd counting has garnered significant interest for video surveillance. This paper proposes a novel scene invariant crowd counting algorithm designed for high accuracy yet low computational complexity in order to facilitate widespread use in real-time embedded video analytics systems. A novel low-complexity, scale-normalized feature called Histogram of Moving Gradients (HoMG) is introduced for highly effective spatiotemporal representation of crowds within a video. Real-time crowd region detection is achieved via boosted cascade of weak classifiers based on HoMG features. Based on the detected crowd regions, linear support vector regression (SVR) of crowd-region HoMG features is introduced for real-time crowd counting. Experimental results using a multi-scene crowd dataset show that the proposed algorithm outperforms state-of-the-art crowd counting algorithms while embedded on modern surveillance cameras. Thus demonstrating the efficacy of the proposed method for accurate, real-time, embedded crowd analysis." @default.
- W2460996549 created "2016-07-22" @default.
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- W2460996549 date "2016-06-01" @default.
- W2460996549 modified "2023-09-25" @default.
- W2460996549 title "Real-Time, Embedded Scene Invariant Crowd Counting Using Scale-Normalized Histogram of Moving Gradients (HoMG)" @default.
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- W2460996549 doi "https://doi.org/10.1109/cvprw.2016.115" @default.
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