PPT-Multi-Local Feature Manifolds for Object Detection

Author : min-jolicoeur | Published Date : 2017-10-18

Oscar Danielsson osda02csckthse Stefan Carlsson stefanccsckthse Josephine Sullivan sullivancsckthse DICTA08 The Problem Object categories are often modeled

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Multi-Local Feature Manifolds for Object Detection: Transcript

Oscar Danielsson osda02csckthse Stefan Carlsson stefanccsckthse Josephine Sullivan sullivancsckthse DICTA08 The Problem Object categories are often modeled by collections bagoffeatures or constellations pictorial structures of local features . Video Analytics. Why Video Analytics?. The increasing rate of crime calls for effective security measures.. Security Personnel, IP Cameras, CCTV are usually employed for these reasons.. But Human vigilance is required in each case which is bound to induce errors. . Image Processing . Pier Luigi . Mazzeo. pierluigi.mazzeo@. cnr.it. Find. Image . Rotation. and Scale Using . Automated. . Feature. . Matching. and RANSAC. Step. 1: Read . Image. original. = . Binarized. Normed Gradients for . Objectness. Estimation at 300fps. CVPR 2014 Oral. Outline. 1. . Introduction. 2.. . Methodology. 2.1 Normed . gradients (NG) and . objectness. 2.2 Learning . objectness. Ross . Girshick. , Jeff Donahue, Trevor Darrell, . Jitandra. Malik (UC Berkeley). Presenter: . Hossein. . Azizpour. Abstract. Can CNN improve . s.o.a. . object detection results?. Yes, it helps by learning rich representations which can then be combined with computer vision techniques.. Oscar . Danielsson. (osda02@kth.se). Stefan . Carlsson. (. stefanc@kth.se. ). Outline. Detect all Instances of an Object Class. The classifier needs to be fast (on average). This is typically accomplished by:. 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]. Image Processing. Pier Luigi Mazzeo. pierluigi.mazzeo@cnr.it. Image Rotation &. Object . Detection . Find. Image . Rotation. and Scale Using . Automated. . Feature. . Matching. and RANSAC. Step. 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. Facebook AI Research. Wenchi. Ma. Data: 11/04/2016. More information from object detection. More information from object detection. More information from object detection. Object Detection for now with Deep Learning. 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. HOGgles Visualizing Object Detection Features C. Vondrick , A. Khosla , T. Malisiewicz , A. Torralba ICCV , 2013 . presented by Ezgi Mercan Object Detection Failures Why do our detectors think water looks like a car? AdaScale: Towards Real-time Video Object Detection using Adaptive Scaling Ting-Wu (Rudy) Chin* Ruizhuo Ding* Diana Marculescu ECE Dept., Carnegie Mellon University SysML 2019 Autonomous Cars Computer Vision, FCUP, . 2018/19. Miguel Coimbra. Slides by Prof. Kristen . Grauman. Today. Local . invariant . features. Detection of interest points. (Harris corner detection). Scale invariant blob detection: . CS5670: Computer Vision. Announcements. Project 1 code due Thursday, 2/25 at 11:59pm. Turnin. via . Github. Classroom. Project 1 artifact due Monday, 3/1 at 11:59pm. Quiz this Wednesday, 2/24, via Canvas.

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