Alle Publikationen
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2017
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(2017): Social Scene Understanding: End-to-End Multi-Person Action Localization and Collective Activity Recognition. Online verfügbar unter http://arxiv.org/pdf/1611.09078v1
Abstract: We present a unified framework for understanding human social behaviors in raw image sequences. Our model jointly detects multiple individuals, infers their social actions, and estimates the collective actions with a single feed-forward pass through a neural network. We propose a single architecture that does not rely on external detection algorithms but rather is trained end-to-end to generate dense proposal maps that are refined via a novel inference scheme. The temporal consistency is handled via a person-level matching Recurrent Neural Network. The complete model takes as input a sequence of frames and outputs detections along with the estimates of individual actions and collective activities. We demonstrate state-of-the-art performance of our algorithm on multiple publicly available benchmarks.
2016
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(2016): Knowledge Transfer for Scene-specific Motion Prediction. Online verfügbar unter http://arxiv.org/pdf/1603.06987v2
Abstract: When given a single frame of the video, humans can not only interpret the content of the scene, but also they are able to forecast the near future. This ability is mostly driven by their rich prior knowledge about the visual world, both in terms of (i) the dynamics of moving agents, as well as (ii) the semantic of the scene. In this work we exploit the interplay between these two key elements to predict scene-specific motion patterns. First, we extract patch descriptors encoding the probability of moving to the adjacent patches, and the probability of being in that particular patch or changing behavior. Then, we introduce a Dynamic Bayesian Network which exploits this scene specific knowledge for trajectory prediction. Experimental results demonstrate that our method is able to accurately predict trajectories and transfer predictions to a novel scene characterized by similar elements.
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(2016) : Learning Social Etiquette: Human Trajectory Understanding In Crowded Scenes In: Leibe, Frankie: Computer vision - ECCV 2016: 14th European Conference, Amsterdam, the Netherlands, October 11-14, 2016 : proceedings, 9912: Cham, Switzerland: Springer (LNCS sublibrary: SL6 - Image processing, computer vision, pattern recognition, and graphics), S. 549-565
DOI: https://doi.org/10.1007/978-3-319-46484-8_33 Abstract: Humans navigate crowded spaces such as a university campus by following common sense rules based on social etiquette. In this paper, we argue that in order to enable the design of new target tracking or trajectory forecasting methods that can take full advantage of these rules, we need to have access to better data in the first place. To that end, we contribute a new large-scale dataset that collects videos of various types of targets (not just pedestrians, but also bikers, skateboarders, cars, buses, golf carts) that navigate in a real world outdoor environment such as a university campus. Moreover, we introduce a new characterization that describes the “social sensitivity” at which two targets interact. We use this characterization to define “navigation styles” and improve both forecasting models and state-of-the-art multi-target tracking–whereby the learnt forecasting models help the data association step.
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