PPT-Vision-based 3d bicycle tracking using deformable part model and interacting multiple

Presentation by Jonathan Kaan DeBoy Paper by Hyunggi Cho Paul E Rybski and Wende Zhang 1 Motivation B uild understanding of surrounding D etect vulnerable road users VRU B icyclist M. Li Center for Biometrics and Security Research National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences China jjyanzleilywenszli nlpriaaccn Abstract This paper solves the speed bottleneck of deformable part mod Pedro F. . Felzenszwalb. & Daniel P. . Huttenlocher. - A Discriminatively Trained, . Multiscale. , Deformable Part Model. Pedro . Felzenszwalb. , David . McAllester. Deva. . Ramanan. Presenter: . P. . Felzenszwalb. Generic object detection with deformable part-based models. Challenge: Generic object detection. Histograms of oriented gradients (HOG). Partition image into blocks at multiple scales and compute histogram of gradient orientations in each block. Discriminative part-based models. Many slides based on . P. . . Felzenszwalb. Challenge: Generic object detection. Pedestrian detection. Features: Histograms of oriented gradients (HOG). Partition image into 8x8 pixel blocks and compute histogram of gradient orientations in each block. CONSTRAINT PROGRAMMING-Optimization . based infeasibility diagnosis framework for . nonconvex. NLPs and MINLPs. Yash. . Puranik. Advisor: Nick . Sahinidis. The authors would like to thank Air . Liquide. for Object Detection. Forrest Iandola, . Ning. Zhang, Ross . Girshick. , Trevor Darrell, and Kurt . Keutzer. Deformable Parts Model (DPM): state of the art algorithm for object detection [1]. Several attempts to accelerate multi-category DPM detection, such as [2] [3]. P. . Felzenszwalb. Object detection with deformable part-based models. Challenge: Generic object detection. Histograms of oriented gradients (HOG). Partition image into blocks and compute histogram of gradient orientations in each block. Object Localization. Goal: detect the location of an object within an image. Fully supervised:. Training data labeled with object category and ground truth bounding boxes. Weakly supervised:. Only object category is known, no location info. ). Felzenswalb. , . Girshick. , . McAllester. & . Ramanan. (2010). Slides drawn from a tutorial By R. . Girshick. AP 12% 27% 36% 45% 49%. 2015. 2. 12.. Jeany Son. References. Bottom-up Segmentation for Top-down . Detection, CVPR 2013. Segmentation-aware Deformable Part Models, CVPR 2014. 2. Prior Works on Segmentation & Recognition. A toolbox for evaluating bicycle related travel. 15. th. TRB Planning Applications Conference. May 17. , 2015. Moby Khan, . Srinath. . Ravulaparthy. , Feng Liu and Tom Rossi. Robert Cálix, Chaushie Chu, Robert Farley. Department of Electrical and Computer Engineering. Bradley University. 5/3/2017. Anthony Le and Ryan Clue. Advisors: Dr. Jing Wang and Dr. In Soo . Ahn. Introduction . Overview of QBot2. Control Design. Pedro F. . Felzenszwalb. & Daniel P. . Huttenlocher. - A Discriminatively Trained, . Multiscale. , Deformable Part Model. Pedro . Felzenszwalb. , David . McAllester. Deva. . Ramanan. Presenter: . Marco Pedersoli Andrea Vedaldi Jordi Gonzàlez. [Fischler Elschlager 1973]. Object detection. 2. 2. Addressing the computational bottleneck. branch-and-bound . [Blaschko Lampert 08, Lehmann et al. 09].