Reliable Human Detection and Tracking in Top-View Depth Images

2013 ◽  
Author(s):  
Michael Rauter
Keyword(s):  
Human Detection ◽  
Depth Images ◽  
Detection And Tracking ◽  
Top View

Towards Silhouette-Aware Human Detection in Depth Images

2021 ◽  
Author(s):  
Huan Luo ◽  
Shuiwang Li ◽  
Qijun Zhao
Keyword(s):  
Human Detection ◽  
Depth Images

scholarly journals Privacy-Aware Human-Detection and Tracking System Using Biological Signals

2019 ◽  
Vol E102.B (4) ◽  
pp. 708-721
Author(s):  
Toshihiro KITAJIMA ◽  
Edwardo Arata Y. MURAKAMI ◽  
Shunsuke YOSHIMOTO ◽  
Yoshihiro KURODA ◽  
Osamu OSHIRO
Keyword(s):  
Tracking System ◽  
Human Detection ◽  
Detection And Tracking ◽  
Biological Signals

scholarly journals Convolutional Neural Network Using Kalman Filter for Human Detection and Tracking on RGB-D Video

2018 ◽  
Vol 12 (2) ◽  
pp. 105 ◽  
Author(s):  
Jovin Angelico ◽  
Ken Ratri Retno Wardani
Keyword(s):  
Neural Network ◽  
Kalman Filter ◽  
Convolutional Neural Network ◽  
Human Detection ◽  
Detection Accuracy ◽  
Tracking Accuracy ◽  
Lighting Condition ◽  
Detection And Tracking ◽  
Computation Efficiency ◽  
The Impact

The computer ability to detect human being by computer vision is still being improved both in accuracy or computation time. In low-lighting condition, the detection accuracy is usually low. This research uses additional information, besides RGB channels, namely a depth map that shows objects’ distance relative to the camera. This research integrates Cascade Classifier (CC) to localize the potential object, the Convolutional Neural Network (CNN) technique to identify the human and nonhuman image, and the Kalman filter technique to track human movement. For training and testing purposes, there are two kinds of RGB-D datasets used with different points of view and lighting conditions. Both datasets have been selected to remove images which contain a lot of noises and occlusions so that during the training process it will be more directed. Using these integrated techniques, detection and tracking accuracy reach 77.7%. The impact of using Kalman filter increases computation efficiency by 41%.

POSE ROBUST HUMAN DETECTION IN DEPTH IMAGES USING MULTIPLY-ORIENTED 2D ELLIPTICAL FILTERS

2010 ◽  
Vol 24 (05) ◽  
pp. 691-717 ◽  
Author(s):  
SANG-HO CHO ◽  
TAEWAN KIM ◽  
DAIJIN KIM
Keyword(s):  
Human Identification ◽  
Human Detection ◽  
Stereo Images ◽  
Bhattacharyya Distance ◽  
Identification Method ◽  
Depth Images ◽  
Detection And Identification ◽  
The Face ◽  
Human Pose ◽  
Spatial Depth

This paper proposes a pose robust human detection and identification method for sequences of stereo images using multiply-oriented 2D elliptical filters (MO2DEFs), which can detect and identify humans regardless of scale and pose. Four 2D elliptical filters with specific orientations are applied to a 2D spatial-depth histogram, and threshold values are used to detect humans. The human pose is then determined by finding the filter whose convolution result was maximal. Candidates are verified by either detecting the face or matching head-shoulder shapes. Human identification employs the human detection method for a sequence of input stereo images and identifies them as a registered human or a new human using the Bhattacharyya distance of the color histogram. Experimental results show that (1) the accuracy of pose angle estimation is about 88%, (2) human detection using the proposed method outperforms that of using the existing Object Oriented Scale Adaptive Filter (OOSAF) by 15–20%, especially in the case of posed humans, and (3) the human identification method has a nearly perfect accuracy.

Adaptive Filtering Methods for RSSI Signals in a Device-Free Human Detection and Tracking System

2019 ◽  
Vol 13 (3) ◽  
pp. 2998-3009 ◽  
Author(s):  
Apidet Booranawong ◽  
Nattha Jindapetch ◽  
Hiroshi Saito
Keyword(s):  
Adaptive Filtering ◽  
Tracking System ◽  
Human Detection ◽  
Detection And Tracking ◽  
Device Free
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