Body-part Tubelet Transformer for Human-Related Crime Classification

Abstract

Detecting human-related crimes from surveillance videos poses an increasingly difficult challenge, especially when confronted with human actions that are relatively similar. In this work, we propose a transformer-based model that induces bias through the incorporation of a Tubelet embedder module-a 3D convolutional layer. The aim is to capture spatiotemporal embeddings from skeletal trajectories extracted from videos using 3D convolutional operations. Our experiments are conducted on the Human-Related Crime dataset, revealing that the use of tubelet embeddings maintains competitive performance (49% accuracy) to the state-of-the-art, while considerably reducing the computational complexity of the model.