'Human detection' diaporamas de présentation

Recognition II

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Presenter : Shiu , Jia-Hau Advisor : Wang, Sheng-Jyh

Simultaneous surveillance camera calibration and foot-head homology estimation from human detection 1 Author : Micusic & Pajdla. Presenter : Shiu , Jia-Hau Advisor : Wang, Sheng-Jyh. 1. 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Outline.

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EECS 274 Computer Vision

EECS 274 Computer Vision. Object detection. Human detection. HOG features Cue integration Ensemble of classifiers ROC curve Reading: Assigned papers. Human detection with HOG. Histogram of oriented gradients Using local gradients to represent positive and negative examples.

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EECS 274 Computer Vision

EECS 274 Computer Vision. Object detection. Human detection. HOG features Cue integration Ensemble of classifiers ROC curve Reading: Assigned papers. Human detection with HOG. Histogram of oriented gradients Using local gradients to represent positive and negative examples.

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BING: Binarized Normed Gradients for Objectness Estimation at 300fps

BING: Binarized Normed Gradients for Objectness Estimation at 300fps. Ming-Ming Cheng 1 Ziming Zhang 2 Wen-Yan Li 1 Philip H. S. Torr 1 1 Torr Vision Group, Oxford University 2 Boston University. Motivation: Generic object detection. Motivation: What is an object?.

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Discriminative Local Binary Patterns for Human Detection in Personal Album

Discriminative Local Binary Patterns for Human Detection in Personal Album. 20101230. The main contribution of our work is along the first direction, i.e. we focus on building more powerful features for human detection.

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Person De-Identification in Videos

Person De-Identification in Videos. Prachi Agrawal and P. J. Narayanan IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, VOL. 21, NO. 3, MARCH 2011. Person De-Identification in Videos. Outline. Introduction De-Identification: General Framework

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Human tracking and counting using the KINECT range sensor based on Adaboost and Kalman Filter

Human tracking and counting using the KINECT range sensor based on Adaboost and Kalman Filter. ISVC 2013. Problem . Human tracking . Avoid occlusion. Human Detection. Observations: There is an empty space in the front and back of head

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Presentation 5

Presentation 5. Ruben Villegas 06/26/2012. Papers. Articulated pose estimations with flexible mixture-of-parts, by Yin Yang and Deva Raman An HOG-LBP Human Detector with Partial Occlusion Handling, by Xiaoyu Wang, Tony X. Han and Shuicheng Yang.

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DetectorPLS version 0.1.1

DetectorPLS version 0.1.1. ( project webpage ). Author: William Robson Schwartz ( schwartz@cs.umd.edu ). Capabilities. Perform object detection (studied for humans and faces). Learn new PLS models for classes of objects. Good for: Objects that could have templates designed for. Bad for:

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FPGA Based Pedestrian D etection using Histogram of Oriented Gradients (HOG)

FPGA Based Pedestrian D etection using Histogram of Oriented Gradients (HOG). Pedestrian Detection algorithm will be implemented using HOG. The purpose of the project is to have robust pedestrian detector using parallel computation with FPGA. Koray Ozcan.

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Alive Human being detector in war fields

The main of the project is to develop an embedded human alive detection system in war fields by using PIR sensor https://jobschat.in/ is a Place where you can find latest Government Jobs and Private Job , You can search Latest Recruitment Notifications , Admit Cards , Results , Central Government Jobs , State Wise Public sector Unit Vacancies , Police Jobs , Navy Jobs , Army Jobs , All Government Previous Papers , syllabus , Placement Papers for IT Companies and Many more, Explore More Jobs at Jobschat.in Exclusively. The working nature of our project is that the pyroelectric sensor which is connected to our microcontroller embedded board is sensed whenever a person moves. In war areas, so many will be alive. But nobody will take<br>care of those. Our project will be in use at that times. Whenever alive persons detected by the PIR sensor microcontroller gives a buzzer sound.

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Overview

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Real-Time Activity Monitoring of Inpatients

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Our Framework 2.1. Enforcing Temporal Consistency by Post Processing

Human Detection in Videos using Spatio -Temporal Pictorial Structures Amir Roshan Zamir , Afshin Dehghan , Ruben Villegas University of Central Florida. Results Videos Taken From TRECVID MED11 Dataset. 2.2. Enforcing Temporal Consistency by Embedding them into the Detection Process

129 views • 1 slides