Dataset Hub

This dataset provides a large collection of video data on illegal parking causing traffic blockages, aiming to enhance the intelligence of traffic management systems.

This dataset is used for recognizing the action of people climbing over fences, applicable to various security scenarios.

This dataset is used to detect whether passengers or drivers check the blind spot before opening the door, enhancing traffic safety.

This dataset focuses on the automatic recognition of fighting and brawling behaviors to enhance security surveillance efficiency.

This dataset is used to recognize the grasping path, posture, and gripping actions of a robotic arm on objects.

This dataset aims to recognize the usage of safety belts by workers during high-altitude operations.

This dataset is used to identify whether workers are wearing helmets and if they are wearing them correctly, supporting intelligent monitoring and safety management in the security field.

This dataset focuses on monitoring abnormal behavior of suspicious individuals in non-open areas.

This dataset focuses on the recognition and analysis of industrial packaging sealing actions.

This dataset focuses on the detection and analysis of abnormal events involving the uncontrolled movement of large machinery.

This dataset is used to detect stall and obstruction behaviors within transportation hub stations to improve traffic management efficiency.

This dataset is used for identifying parking or slow-moving behaviors in tunnels.

This dataset is used to detect high-pressure gas leak events, aiding in industrial safety monitoring and management.

This dataset monitors the consistency of actions in standard assembly steps, aiming to enhance industrial automation levels.

This dataset monitors prolonged congestion or gathering of people in the underground garage due to queuing, payment, charging, and other reasons.

This dataset contains video data of motor vehicle and non-motor vehicle collision events aimed at enhancing traffic safety monitoring and accident prevention capabilities.

This dataset is used to identify irregular vibrations, abnormal sounds, jumps, and other precursor behaviors of faults during the operation of fans.

This dataset is dedicated to identifying abnormal states of traffic lights, providing reliable data support for traffic monitoring.

This dataset focuses on the detection and analysis of short-circuit events in electrical equipment, assisting in safety monitoring and equipment maintenance.

The Robbery and Coercion Action Recognition Dataset aims to help the security industry improve its ability to detect violent behavior.