Spotting Strange Events in Big Video Data

Video cameras are vital for safety, but with so many hours of footage, figuring out what's normal and what's not can be tough. This research uses the power of ResNet50, VGG19, EfficientNetB7, and the superstar ViT_b16 to smartly shrink video files. By pinpointing key frames, the system avoids exhaustive monitoring. It's like skimming through a story instead of reading every word. Large-scale video testing on UCF-Crime dataset showed impressive accuracy boosts. EfficientNetB7 hit 86. 34%, VGG19 upped it to 87. 90%, and ResNet50 jumped to 90. 46%. But ViT_b16 took the lead with 95. 87%! That's like catching the curiosity of a cat.

Why these models? They whisk through frames with ease, flagging weird events swiftly. Security systems rely on humans, but humans get tired. That's where these models shine – spotting danger without burning out. Plus, they lower costs! This isn't your average approach. It's like a superhero team tackling the mountain of video data. It eliminates unnecessary frames, making the process swift and smart. The future looks safer and simpler with this tech.

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