RW-SVD: A surround view rough weather video anomaly dataset and a brief overview of existing datasets

Sachin Dube, Dinesh Kumar Tyagi, Ramesh Babu Battula; Proceedings of the Asian Conference on Computer Vision (ACCV) Workshops, 2024, pp. 143-159

Modern surveillance societies constantly face bottlenecks due to manual monitoring of huge amounts of data generated by surveillance infrastructure. The limitation of manual monitoring is further aggravated by challenging weather conditions such as fog, rain, mist, etc. This gave rise to automated surveillance making Video anomaly detection (VAD) one of the most sought-after domains in computer vision. The availability of data that contain weather-induced variations is a key factor in the effectiveness of Data-driven approaches that rely on data for precise modeling. To this end, we have presented a brief review of previous datasets and their limitations on parameters such as size, scene variations, activities covered, effect of weather phenomena, etc. To leverage the intricate relationship between data and model we present a novel human-centric surround view dataset where each scripted activity is recorded simultaneously by 4 strategically placed cameras to capture effects of varying distance, angle, height, and illumination on the same scene. The proposed dataset is arranged into 4 abnormal classes namely fighting, snatching, panic running, and kidnapping. It contains complex backgrounds, reallife objects (cycle, motorbike, four-wheeler), both indoor and outdoor environments as well as illumination change. To tackle ambiguity during the transition from normal to abnormal or vice-versa we conducted voting (subjective evaluation) with 10 volunteers.We further augmented the dataset with two of the most common weather phenomena namely haze and rain to bridge the gap between real-world challenges and dataset.