A Framework for the Video Surveillance Suspicious Activity Detection

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Achieving 97.96% accuracy in recognizing and categorizing actions demonstrates significant advancements in video surveillance technology.
Normal actions like sitting and running were distinguished from suspicious actions such as shoplifting, highlighting the system's effectiveness.
Utilizing a convolutional neural network for real-time image analysis provides a method for identifying various human behaviors.
Further refinement of the model may enable advanced behavior prediction and quicker responses to potential threats.

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Observational analysis detects suspicious behavior in video surveillance, indicating potential security enhancements.

Rohitaksha et al. (2025) studied this question.

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