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Hybrid deep learning and HOF for Anomaly Detection

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Abstract

Anomalies detection in video footage is a daunting task treated with many challenges in crowded scenes. In this paper, we propose an efficient method based on deep learning and handcrafted spatio-temporal feature extraction for anomaly detection using a pre-trained CNN (convolution neural network) and HOF (Histogram of Optical Flow) features. Abnormal motion is picked by relative thresholding. One-class SVM is trained with spatial features for robust classification of abnormal shapes. Moreover, a decision function is applied to correct the false alarms and the miss detections. Our method has a high performance in terms of speed and accuracy. It achieved anomaly detection with good efficiency in challenging datasets and reduced computational complexity compared to state-of-the-art methods.
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Dates and versions

hal-02279226 , version 1 (05-09-2019)

Identifiers

  • HAL Id : hal-02279226 , version 1

Cite

Slim Hamdi, Samir Bouindour, Kais Loukil, Hichem Snoussi, Mohamed Abid. Hybrid deep learning and HOF for Anomaly Detection. 2019 6th International Conference on Control, Decision and Information Technologies (CoDIT), Apr 2019, Paris, France. pp.575-580. ⟨hal-02279226⟩
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