Complex Pairwise Activity Analysis Via Instance Level Evolution Reasoning

Video activity analysis systems are often trained on large datasets. Activities and events in the real-world do not occur in isolation, instead, they occur as interactions between related objects. This work introduces a novel method that jointly exploits relational information between pairs of objects and temporal dynamics of each object. The proposed method effectively leverages a new simple architecture that is flexible and easily trained to detect relational activities and events using small datasets (hundreds of samples). The solution is constructed and tested using synthetic videos of car-collision events. The annotated datasets in this work will be made available online to the research community. Experimental results demonstrate the efficacy of the network to perform complex activity analysis.