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arXiv AI By S Mahmudul Hasan, Lu Peng

CrowdCC: Jointly Benchmarking Corruption Robustness and Deployment Quantization in Crowd Counting

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CrowdCC is a benchmark that jointly evaluates corruption robustness and deployment quantization for crowd‑counting models on edge devices. It introduces 23 deployment‑motivated corruptions at three severity levels, tests 26 models across four datasets, and measures performance in FP32, FP16, and INT8 precision on A100 TensorRT engines and Jetson Orin NX hardware. The study finds that capture‑chain corruptions cause the greatest degradation, INT8 quantization alters robustness unpredictably, and that model selection must consider both corruption robustness and deployment cost together.

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