PPT-Object Detection with Discriminatively Trained Part Based Models

Pedro F Felzenszwalb Ross B Girshick David McAllester and Deva Ramanan Motivation Problem Detecting and localizing generic objects from categories eg people cars etc in static images. Felzenszwalb Ross B Girshick David McAllester and Deva Ramanan Abstract We describe an object detection system based on mixtures of multiscale deformable part models Our system is able to represent highly variable object classes and achieves stateof 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. :. Detecting Real-World States with Lousy Wireless Cameras. Benjamin Meyer, . Richard Mietz. , Kay Römer. 1. Introduction. Motivation. Challenges. System Architecture. Evaluation. Structure. 2. Towards the Internet of Things. Hamed Pirsiavash and Deva . Ramanan. Department of Computer Science. UC Irvine . 2. Deformable . part models . (DPM). Human pose estimation. Face pose estimation. Object detection. Felzenszwalb. , . Girshick. applications. The 10th IEEE Conference on Industrial Electronics and Applications (ICIEA 2015. ), Auckland , 15-17 June 2015. Kai Ki Lee. 1. ,Ying Kin Yu. 2. and Kin Hong . Wong. 2+. 1. Dept. of Information Engineering, The Chinese University of Hong Kong (CUHK). 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]. Before deep . convnets. Using deep . convnets. PASCAL VOC. Beyond sliding windows: Region proposals. Advantages:. Cuts . down on number of regions detector must . evaluate. Allows detector to use more powerful features and classifiers. Before deep . convnets. Using deep . convnets. PASCAL VOC. Beyond sliding windows: Region proposals. Advantages:. Cuts . down on number of regions detector must . evaluate. Allows detector to use more powerful features and classifiers. 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. Bangpeng Yao and Li Fei-Fei. Computer Science Department, Stanford University. {bangpeng,feifeili}@cs.stanford.edu. 1. Robots interact with objects. Automatic sports commentary. “Kobe is dunking the ball.”. Towards a Masquerade Detection System Based on User’s Tasks J. Benito Camiña , Jorge Rodr íguez, and Raúl Monroy Presentation by Calvin Raines What is a masquerade attack? password123 Hello <Your Name Here> Authors. Bo Sun, Fei Yu, Kui Wu, Yang Xiao, and Victor C. M. Leung.. . Presented by . Aniruddha Barapatre. Introduction. Importance of Cellular phones.. Due to the open radio transmission environment and the physical vulnerability of mobile devices , . Xindian. Long. 2018.09. Outline. Introduction. Object Detection Concept and the YOLO Algorithm. Object Detection Example (CAS Action). Facial Keypoint Detection Example (. DLPy. ). Why SAS Deep Learning .