PDF-The Fastest Deformable Part Model for Object Detection

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. Felzenszwalb University of Chicago pffcsuchicagoedu Ross B Girshick University of Chicago rbgcsuchicagoedu David McAllester TTI at Chicago mcallestertticedu Abstract We describe a general method for building cascade clas si64257ers from partbased de Felzenszwalb University of Chicago pffcsuchicagoedu Ross B Girshick University of Chicago rbgcsuchicagoedu David McAllester TTI at Chicago mcallestertticedu Abstract We describe a general method for building cascade clas si64257ers from partbased de 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. Large Scale Visual Recognition Challenge (ILSVRC) 2013:. Detection spotlights. Toronto A team. Latent Hierarchical Model with GPU Inference for Object Detection. Yukun Zhu, Jun Zhu, Alan Yuille . UCLA Computer Vision Lab. 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. ). 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. 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. Pedro F. . Felzenszwalb. & Daniel P. . Huttenlocher. - A Discriminatively Trained, . Multiscale. , Deformable Part Model. Pedro . Felzenszwalb. , David . McAllester. Deva. . Ramanan. Presenter: . Pedro F. . Felzenszwalb. , Ross B. . Girshick. , David . McAllester. , and Deva . Ramanan. Motivation. Problem: Detecting and localizing generic objects from categories (e.g. people, cars, etc.) in static images.. 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].