Anomaly detection in crowded scenes

# Anomaly detection in crowded scenes > OpenAlex Metadata Hub · https://openalex.org/W2122361470 ## Bibliographic - **DOI:** 10.1109/cvpr.2010.5539872 - **Year:** 2010 - **Citations:** 1519 - **Open Access:** No (closed) - **License:** — - **Source:** https://doi.org/10.1109/cvpr.2010.5539872 ## Authors - Vijay Mahadevan - Weixin Li - Viral Bhalodia - Nuno Vasconcelos ## Abstract A novel framework for anomaly detection in crowded scenes is presented. Three properties are identified as important for the design of a localized video representation suitable for anomaly detection in such scenes: (1) joint modeling of appearance and dynamics of the scene, and the abilities to detect (2) temporal, and (3) spatial abnormalities. The model for normal crowd behavior is based on mixtures of dynamic textures and outliers under this model are labeled as anomalies. Temporal anomalies are equated to events of low-probability, while spatial anomalies are handled using discriminant saliency. An experimental evaluation is conducted with a new dataset of crowded scenes, composed of 100 video sequences and five well defined abnormality categories. The proposed representation is shown to outperform various state of the art anomaly detection techniques. ## Keywords Anomaly detection, Artificial intelligence, Computer science, Outlier, Anomaly (physics), Pattern recognition (psychology), Abnormality, Representation (politics), Computer vision, Discriminant ## Concepts - Anomaly detection - Artificial intelligence - Computer science - Outlier - Anomaly (physics) - Pattern recognition (psychology) - Abnormality - Representation (politics) - Computer vision - Discriminant - Political science - Condensed matter physics - Law - Politics - Physics - Psychology - Social psychology --- *Metadata only — full text not imported unless Open Access license permits.*

Bài “Anomaly detection in crowded scenes” được TradingBase chuyển thành Knowledge Product cho trader — không phải trang đọc abstract OpenAlex. Tóm lược học thuật (đã diễn giải): A novel framework for anomaly detection in crowded scenes is presented. Three properties are identified as important for the design of a localized video representation suitable for anomaly detection in such scenes: (1) joint modeling of appearance and dynamics of the scene, and the abilities to detect (2) temporal, and (3) spatial abnormalities. The model for normal crowd behavior is based on mixtures of dynamic textures and outliers under this model are labeled as anomalies. Temporal anomalies are equated to events of low-probability, while spatial anomalies are handled using discriminant saliency. An experimental evaluation is conducted with a new dataset of crowded scenes, composed of 100 video sequences and five well defined abnormality categories. The proposed representation is shown to outperform various state of the art anomaly detection techniques. Phần Trading Insights bên dưới nối nghiên cứu với Forex, vàng, USD, lãi suất và risk regime — để bạn đưa vào journal và playbook. Metadata DOI/OA chỉ là rail tham chiếu; nội dung chính là summary, takeaways và ứng dụng thị trường do Content Factory sinh.