This work was mainly done in the Omni Lab for Intelligent Visual Engineering and Science (OLIVES) @ Georgia Tech.
Feel free to check our lab's Website and GitHub for other interesting work!!!
C. Zhou, G. AlRegib, A. Parchami, and K. Singh, "Learning Trajectory-Conditioned Relations to Predict Pedestrian Crossing Behavior," in IEEE International Conference on Image Processing (ICIP), Bordeaux, France, Oct. 16-19 2022.
In smart transportation, intelligent systems avoid potential collisions by predicting the intent of traffic agents, espe- cially pedestrians. Pedestrian intent, defined as future action, e.g., start crossing, can be dependent on traffic surround- ings. In this paper, we develop a framework to incorporate such dependency given observed pedestrian trajectory and scene frames. Our framework first encodes regional joint information between a pedestrian and surroundings over time into feature-map vectors. The global relation representations are then extracted from pairwise feature-map vectors to esti- mate intent with past trajectory condition. We evaluate our approach on two public datasets and compare against two state-of-the-art approaches. The experimental results demon- strate that our method helps to inform potential risks during crossing events with 0.04 improvement in F1-score on JAAD dataset and 0.01 improvement in recall on PIE dataset. Fur- thermore, we conduct ablation experiments to confirm the contribution of the relation extraction in our framework.
Training/validation: Run the following command in a bash script.
python3 core/main_intent.py --config_file configs_intent/shallowCNN/rn_PIE.yml --gpu 0