Conference paper Open Access

Spatio-temporal activity detection and recognition in untrimmed surveillance videos

Konstantinos Gkountakos; Despoina Touska; Konstantinos Ioannidis; Theodora Tsikrika; Stefanos Vrochidis; Ioannis Kompatsiaris

This work presents a spatio-temporal activity detection and recognition framework for untrimmed surveillance videos consisting of a three-step pipeline: object detection, tracking, and activity recognition. The framework relies on the YOLO v4 architecture for object detection, Euclidean distance for tracking, while the activity recognizer uses a 3D Convolutional Deep learning architecture employing spatio-temporal boundaries and addressing it as multi-label classification. The evaluation experiments on the VIRAT dataset achieve accurate detections of the temporal boundaries and recognitions of activities in untrimmed videos, with better performance for the multi-label compared to the multi-class activity recognition.

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