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A comparison between neuromorphic vision and traditional still-frame cameras to understand what is best as a modern surveillance tool.
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Most video surveillance still runs on traditional still-frame cameras that capture full frames at a fixed rate. Neuromorphic (event) cameras work differently: each pixel fires only when its brightness changes, mimicking the human retina and outputting a sparse stream of events instead of frames. Independently reported specs back the advantages including ~120–140 dB dynamic range vs. ~60 dB, microsecond latency, chip-level power in the tens of milliwatts, and effectively no motion blur. In practice, this changes real surveillance work: intrusion detection with fewer false alarms, sensor-level background subtraction for anomaly detection, reliable capture in brutal lighting, and always-on monitoring on tiny power and bandwidth budgets. The catch: no absolute intensity, lower resolution, and non-standard output that human operators can't read directly. The pragmatic winner is a hybrid system, an always-on event sensor that wakes and cues a high-resolution frame camera when something moves. Commercial parts (iniVation DAVIS, Sony/Prophesee IMX636) already exist to build it