Content oriented 3D-CNN sequence learning architecture for academic activities recognition using a realistic CAD dataset

In computer vision, video analytic researchers have been developing techniques for human activity recognition in several application domains. Academic institutions are in possession of rich video repository generated by the surveillance system in respective campuses. One major requirement is to develop lightweight adaptable models capable of recognizing academic activities, enabling effective decision making in various application domains. This research article proposes a lightweight 3D-CNN architecture for recognizing a novel set of academic activities using a realistic campus video dataset. The proposed sequence learning model is obtained by utilizing spatial and temporal video information enabling accurate classification of the target activity sequences. The proposed model is compared with the LSTM model, a state-of-the-art algorithm for time-series and sequence-learning problems, by performing sufficient experimentations. Experimental results demonstrate that the proposed 3D-CNN model outperforms other variants, achieving the highest accuracy of 95%, minimum computational cost of 13.3 GFLOPs, and low memory overhead of 18,464 KB. These performance indicators make the proposed model an efficient and effective classifier for the proposed academic activity recognition task.