← All IntelClip / AI ToolsSurveillance demo: counting, classifying and detecting near-collisions
From Video Has No Memory. Here's How We Built One. — James Le, TwelveLabs · ≈15:31
Demonstrates structured extraction plus safety-event detection holding up across crowded and rainy footage, the workload where frame-sampling approaches typically degrade.
What’s in it
- Demonstrates structured extraction plus safety-event detection holding up across crowded and rainy footage, the workload where frame-sampling approaches typically degrade.
Clip transcript
But then you can obviously build more interesting and more like real practical application of which security is one that we encounter a lot. So on the on this example I ingest it you know publicly available camera footage. For context this are the clip you have traffic jams suburban you know urban area and given this footage where I asked Jockey to count and classify every vehicle in the intersection break it down by type plus pedestrian. And it returned the number of vehicles and the big foot traffic as well. You can detect safety events right? So you see that a red SUV turn and almost gets struck. Another scene here. Turn left into an upcoming car. Yeah, so that a clear straight line entry. It works well in you know different scenario. This scene is very crowded area in Bangkok. It asked it I also asked it to work on the you know the rain right? So this is another scene where it understanding the um rainy condition. It identify the basis intersection vehicle window. So yeah, those are some example for for camera security surveillance footage.
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