Apratim Bhattacharyya (PhD Student)

Apratim Bhattacharyya

Max-Planck-Institut für Informatik
Saarland Informatics Campus
Campus E1 4
66123 Saarbrücken
E1 4 - Room 627
+49 681 9325 2027
+49 681 9325 2099
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Personal Information


  • 2016-present, Ph.D. candidate in Computer Science, Max Planck Institute for Informatics, Germany
  • 2014-2016, M.Sc.(Honors Degree) in Computer Science, Saarland University, Germany
  • 2010-2014, B.Tech. in Computer Engineering, National Institute of Technology, Karnataka, India


Long-Term Image Boundary Prediction
A. Bhattacharyya, M. Malinowski, B. Schiele and M. Fritz
Thirty-Second AAAI Conference on Artificial Intelligence, 2018
(Accepted/in press)
Long-Term On-Board Prediction of Pedestrians in Traffic Scenes
A. Bhattacharyya, M. Fritz and B. Schiele
1st Conference on Robot Learning (CoRL 2017), 2017
Efficiently Summarising Event Sequences with Rich Interleaving Patterns
A. Bhattacharyya and J. Vreeken
Proceedings of the Seventeenth SIAM International Conference on Data Mining (SDM 2017), 2017
Long-Term On-Board Prediction of People in Traffic Scenes under Uncertainty
A. Bhattacharyya, M. Fritz and B. Schiele
Technical Report, 2017
(arXiv: 1711.09026)
Progress towards advanced systems for assisted and autonomous driving is leveraging recent advances in recognition and segmentation methods. Yet, we are still facing challenges in bringing reliable driving to inner cities, as those are composed of highly dynamic scenes observed from a moving platform at considerable speeds. Anticipation becomes a key element in order to react timely and prevent accidents. In this paper we argue that it is necessary to predict at least 1 second and we thus propose a new model that jointly predicts ego motion and people trajectories over such large time horizons. We pay particular attention to modeling the uncertainty of our estimates arising from the non-deterministic nature of natural traffic scenes. Our experimental results show that it is indeed possible to predict people trajectories at the desired time horizons and that our uncertainty estimates are informative of the prediction error. We also show that both sequence modeling of trajectories as well as our novel method of long term odometry prediction are essential for best performance.
Long Term Boundary Extrapolation for Deterministic Motion
A. Bhattacharyya, M. Malinowski and M. Fritz
NIPS Workshop on Intuitive Physics, 2016