Aggressive Behavior Detection
Scylla Aggressive Behavior Detection swiftly detects movements associated with fights and vandalism, allowing for a rapid response to prevent violence.
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According to the Bureau of Justice Statistics (BJS) of the U.S. Department of Justice, physical assaults make up 83% of nonfatal workplace injuries treated in emergency rooms, totaling 439,000 cases each year.
Workplace violence not only causes physical harm but also carries a heavy economic toll. Estimates suggest it costs up to $56 billion annually, including medical expenses, law enforcement, legal fees, and lost productivity.
The CDC reported that in 2019, injuries in the U.S. resulted in a total cost of $4.2 trillion, covering healthcare expenses and lost productivity.
These figures highlight the urgent need for strong workplace violence prevention and response strategies to protect employees and reduce economic losses. That is why at Scylla, we’ve engineered Scylla Aggressive Behavior Detection solution that is tailored to detect various abnormal behaviors and vandalism.
How it works
Instantly detect violence and prevent potential tragedies
Proprietary Scylla Aggressive Behavior Detection helps you detect various abnormal behaviors associated with fights and vandalism, allowing you to respond quickly to prevent violence.
Outstanding accuracy and tailored approach
Scylla Aggressive Behavior Detection achieves up to 96% accuracy, rapidly identifying aggressive behaviors such as fights, arrests, abuse, and brawling. It also includes a specialized model for vandalism detection, flagging vigorous actions, for instance, striking an ATM with a tool or repeated punching can trigger an alert.
Moreover, our selection of a deep learning architecture harnesses the latest advancements in Large Language Models (LLMs) applied for vision tasks, which ensures high accuracy of at least 96%
Detecting both individual and group anomalies
We’ve developed a custom data processing system that includes a special algorithm and dataset. This setup allows us to easily detect unusual behavior in both individuals and groups, without needing to adjust any settings.
Continuous self-learning
Unlike many solutions that rely on predefined rules or limited feature sets, Scylla Aggressive Behavior Detection system continuously adapts to diverse real-world scenarios by leveraging self-learning mechanisms and real-time feedback loops. This allows it to detect emerging patterns of vandalism and fights with exceptional precision, even in complex, high-density environments.
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