Human detection method based on feature co-occurrence of HLAC and HOG

Moving object detection is the basic of video applications such as computer vision, object recognition and tracking, surveillance security etc. Background subtraction and symmetrical differencing are the popular methods of motion detection. The main idea of them is to compare the current video frame with a specified background image or a background model or the next video frame. For background subtraction, the obtaining of initialization is crucial and many methods have been employed, so it is necessary to model background to adapt the changes of background. In this paper, the single gaussian modeling as the initialization background model combined with an improved linear alternate background updating method is proposed. And then, a novel moving human detection method which employs background subtraction and symmetrical differencing based on rgb color difference model is presented. The experimental results show that the detection method can detect moving human effectively and real-time.

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Fast Human Detection Method in Range Data using Adaptive UV-histogram and Template Matching

Journal of the Institute of Electronics and Information Engineers ◽

10.5573/ieie.2014.51.9.119 ◽

2014 ◽

Vol 51 (9) ◽

pp. 119-128

Author(s):

Bumsik Yoon ◽

Whoi-Yul Kim

Keyword(s):

Template Matching ◽

Detection Method ◽

Human Detection ◽

Range Data

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Human Detection Based on a New Gradient CENTRIST Feature

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.543-547.2716 ◽

2014 ◽

Vol 543-547 ◽

pp. 2716-2719

Author(s):

Tao Li ◽

Tao Xiang ◽

Ren Jie Huang ◽

Xue Zhu Zhao

Keyword(s):

Real Time ◽

Detection Method ◽

Feature Vector ◽

Detection Performance ◽

Human Detection ◽

Feature Descriptor ◽

Image Patch ◽

Method Base

This paper proposes a real-time and accurate human detection method base on a new Gradient CENTRIST feature descriptor. Firstly, the feature can characterizes not only local human appearance and shape but also implicitly represent the global contour. Secondly, it does not involve image pre-processing or feature vector normalization, and it only requires steps to test an image patch. Our main contribution is that a more reliable feature descriptor is found, which can get a better human detection. The experiments on the INRIA pedestrian dataset demonstrate that the detection performance is significantly improved.

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2A2-L04 Robust Human Detection Method in RGB-D Data with HLAC and HOG(Robot Vision(1))

The Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) ◽

10.1299/jsmermd.2012._2a2-l04_1 ◽

2012 ◽

Vol 2012 (0) ◽

pp. _2A2-L04_1-_2A2-L04_4

Author(s):

Miho MORITA ◽

Hiroshi TAKEMURA ◽

Hiroshi MIZOGUCHI

Keyword(s):

Detection Method ◽

Robot Vision ◽

Human Detection

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Human Detection through RSSI Processing with Packet Dropout in Wireless Sensor Network

Journal of Sensors ◽

10.1155/2020/4758103 ◽

2020 ◽

Vol 2020 ◽

pp. 1-9

Author(s):

Haijing Wang ◽

Fangfang Zhang ◽

Wenli Zhang

Keyword(s):

Wireless Sensor Network ◽

Real Time ◽

Sensor Network ◽

Detection Method ◽

Human Detection ◽

Link Quality ◽

Wireless Sensor ◽

Packet Dropout ◽

Data Packets ◽

Device Free

This paper presents a device-free human detection method for using Received Signal Strength Indicator (RSSI) measurement of Wireless Sensor Network (WSN) with packet dropout based on ZigBee. Packet loss is observed to be a familiar phenomenon with transmissions of WSNs. The packet reception rate (PRR) based on a large number of data packets cannot reflect the real-time link quality accurately. So this paper firstly raises a real-time RSSI link quality evaluation method based on the exponential smoothing method. Then, a device-free human detection method is proposed. Compared to conventional solutions which utilize a complex set of sensors for detection, the proposed approach achieves the same only by RSSI volatility. The intermittent Karman algorithm is used to filter RSSI fluctuation caused by environment and other factors in data packets loss situation, and online learning is adopted to set algorithm parameters considering environmental changes. The experimental measurements are conducted in laboratory. A high-quality network based on ZigBee is obtained, and then, RSSI can be calculated from the receive sensor modules. Experimental results show the uncertainty of RSSI change at the moment of human through the network area and confirm the validity of the detection method.

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