BMD-45: A Large-Scale CCTV Vehicle Detection Dataset for Urban Traffic in Developing Cities
Akash Sharma, Chinmay Mhatre, Sankalp Gawali, Ruthvik Bokkasam, Brij Sharma, Vishwajeet Pattanaik, Punit Rathore, Raghu Krishnapuram, Vijay Gopal Kovvali, Anirban Chakraborty, Yogesh Simmhan

TL;DR
BMD-45 is a large-scale, diverse CCTV vehicle detection dataset from developing cities, highlighting the domain gap and aiding robust urban traffic perception.
Contribution
Introduces BMD-45, a comprehensive dataset with 480K annotations across 45K images, including region-specific vehicle categories for underrepresented urban environments.
Findings
Models trained on existing datasets perform poorly on BMD-45.
Fine-tuning on BMD-45 significantly improves detection accuracy.
Domain gap between existing datasets and real-world developing city traffic is substantial.
Abstract
Robust vehicle detection from fixed CCTV cameras is critical for Intelligent Transportation Systems. Yet existing benchmarks predominantly feature relatively homogeneous, highly organized traffic patterns captured from ego-centric driving perspectives or controlled aerial views. This regional and sensor view bias creates a significant gap. Models trained on datasets such as UA-DETRAC and COCO struggle to generalize to the dense, heterogeneous, disorganized traffic conditions observed in rapidly developing urban centers in emerging economies. To address this limitation, we introduce BMD-45, a large-scale dataset comprising 480K bounding boxes annotated over 45K images captured from over 3.6K operational Safe City CCTV cameras. BMD-45 contains 14 fine-grained vehicle categories, including region-specific modes such as auto-rickshaws and tempo travellers, which are not present in existing…
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