Benchmark for Evaluating Pedestrian Action Prediction

Iuliia Kotseruba, Amir Rasouli, John K. Tsotsos; Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2021, pp. 1258-1268

Pedestrian action prediction has been a topic of active research in recent years resulting in many new algorithmic solutions. However, measuring the overall progress towards solving this problem is difficult due to the lack of publicly available benchmarks and common training and evaluation procedures. To this end, we introduce a benchmark based on two public datasets for pedestrian behavior understanding. Using the proposed evaluation procedures, we rank a number of baseline and state-of-the-art models and analyze their performance with respect to various properties of the data. Based on these findings we propose a new model for pedestrian crossing action prediction that uses attention mechanisms to effectively combine implicit and explicit features and demonstrate new state-of-the-art results. The code for models and evaluation is available at https://github.com/ykotseruba/ PedestrianActionBenchmark.