ISVC 2014: Advances in Visual Computing pp 688-697 | Cite as
HLAC between Cells of HOG Feature for Crowd Counting
Abstract
This paper proposes a crowd counting method using higher order auto-correlation (HLAC) feature between cells of histogram oriented gradient (HOG). Although HOG feature is effective for human detection, it depends on the object position and is not suitable for crowd counting. To apply HOG feature to crowd counting, we extract the first-order HLAC feature from cells of HOG feature. Our new feature has shift invariance and additive properties of HLAC feature as well as the robustness to illumination variation of HOG feature. We predict the number of humans in an image using partial least squares regression (PLSR) from our feature. We evaluate our method using the Mall dataset, and we confirmed that our method gives the state-of-art performance.
Keywords
Mean Square Error Partial Little Square Regression Mean Absolute Error Mask Pattern Illumination VariationPreview
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