CHAD: Charlotte Anomaly Dataset

نویسندگان

چکیده

In recent years, we have seen a significant interest in data-driven deep learning approaches for video anomaly detection, where an algorithm must determine if specific frames of contain abnormal behaviors. However, detection is particularly context-specific, and the availability representative datasets heavily limits real-world accuracy. Additionally, metrics currently reported by most state-of-the-art methods often do not reflect how well model will perform scenarios. this article, present Charlotte Anomaly Dataset (CHAD). CHAD high-resolution, multi-camera dataset commercial parking lot setting. addition to frame-level labels, first include bounding box, identity, pose annotations each actor. This especially beneficial skeleton-based which useful its lower computational demand settings. also multiple views same scene. With four camera over 1.15 million frames, largest fully annotated including person annotations, collected from continuous streams stationary cameras smart surveillance applications. To demonstrate efficacy training evaluation, benchmark two algorithms on provide comprehensive analysis, both quantitative results qualitative examination. The available at https://github.com/TeCSAR-UNCC/CHAD.

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ژورنال

عنوان ژورنال: Lecture Notes in Computer Science

سال: 2023

ISSN: ['1611-3349', '0302-9743']

DOI: https://doi.org/10.1007/978-3-031-31435-3_4