The vision of this project is to develop a solution for automated processing of visual sensor data with the goal of endowing visual systems with the ability to be situationally aware. Being truly perceptive of one’s environment from visual cues is a multi-layered process. The ﬁrst task is to observe the scene, i.e., localize and recognize the various agents in the environment. The second task is to understand the scene, i.e., infer ﬁne-grained attributes of each agent such as 3D location, 3D pose, semantic segmentation masks, occlusion masks etc. and the geometric and functional relationship of agent-agent and agent-environment attributes. The third task is to anticipate the future evolution of the environment i.e., predict the likely transfor- Fig. 1 Situational Awareness. mation of the scene based on the functional understanding of the environment and the temporal understanding of the agent’s attributes. The fourth task is to take an appropriate action, i.e., based on the current state of the environment and its likely evolution, take an appropriate action to maximize our ability to truly perceive the environment.
Situational Awareness Overview
Illustration of the various tasks in the situational awareness pipeline and how they are related to each other.
For every grid location, geometrically correct renderings of pedestrian are synthetically generated using known scene information such as camera calibration parameters, obstacles (red), walls (blue) and walkable areas (green). All location-specific pedestrian detectors are trained jointly to learn a smoothly varying appearance model. Multiple scene-and-location-specific detectors are run in parallel at every grid location.
Inferring Visual Attributes through Synthesis: For every small region, physically grounded and geometrically correct renderings of pedestrian are synthetically generated using known scene information such as camera calibration parameters, obstacles (red), walls (blue) and walkable areas (green). We train environment-and-region speciﬁc ShapeNets on the synthetically generated data. At inference, from each image our model will output detections, keypoint locations, occlusion labels and segmentation mask.
Hironori Hattori, Vishnu Naresh Boddeti, Kris Kitani and Takeo Kanade, Learning Scene-Specific Pedestrian Detectors without Real Data (Supplementary Material) (Extended One-Page Abstract), CVPR 2015
Namhoon Lee, Xinshuo Weng, Vishnu Naresh Boddeti, Yu Zhang, Fares Beainy, Kris Kitani and Takeo Kanade, Visual Compiler: Synthesizing a Scene-Specific Pedestrian Detector and Pose Estimator Arxiv 2016