Moving Object Localization in Thermal Imagery by Forward-Backward Motion History Images

2009 ◽  
pp. 271-291 ◽  
Author(s):  
Zhaozheng Yin ◽  
Robert Collins
Keyword(s):  
Moving Object ◽  
Object Localization ◽  
Thermal Imagery ◽  
Motion History Images

scholarly journals General Moving Object Localization from a Single Flying Camera

2020 ◽  
Vol 10 (19) ◽  
pp. 6945
Author(s):  
Kin-Choong Yow ◽  
Insu Kim
Keyword(s):  
Prior Information ◽  
State Of The Art ◽  
Image Sequences ◽  
Specific Pattern ◽  
Visual Surveillance ◽  
Moving Object ◽  
Object Localization ◽  
Stationary Object ◽  
Open Spaces ◽  
Moving Camera

Object localization is an important task in the visual surveillance of scenes, and it has important applications in locating personnel and/or equipment in large open spaces such as a farm or a mine. Traditionally, object localization can be performed using the technique of stereo vision: using two fixed cameras for a moving object, or using a single moving camera for a stationary object. This research addresses the problem of determining the location of a moving object using only a single moving camera, and it does not make use of any prior information on the type of object nor the size of the object. Our technique makes use of a single camera mounted on a quadrotor drone, which flies in a specific pattern relative to the object in order to remove the depth ambiguity associated with their relative motion. In our previous work, we showed that with three images, we can recover the location of an object moving parallel to the direction of motion of the camera. In this research, we find that with four images, we can recover the location of an object moving linearly in an arbitrary direction. We evaluated our algorithm on over 70 image sequences of objects moving in various directions, and the results showed a much smaller depth error rate (less than 8.0% typically) than other state-of-the-art algorithms.

A Moving-Object-Localization Hardware Algorithm Employing OR-Amplification of Pixel Activities

2007 ◽  
Author(s):  
Yusuke Niki ◽  
Yasuo Manzawa ◽  
Satoshi Kametani ◽  
Tadashi Shibata
Keyword(s):  
Moving Object ◽  
Object Localization

scholarly journals Moving Object Localization Using Optical Flow for Pedestrian Detection from a Moving Vehicle

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Joko Hariyono ◽  
Van-Dung Hoang ◽  
Kang-Hyun Jo
Keyword(s):  
Optical Flow ◽  
Pedestrian Detection ◽  
Moving Object ◽  
Object Localization ◽  
Support Vector ◽  
Current Frame ◽  
Moving Vehicle ◽  
Morphological Process ◽  
Optical Flows ◽  
Linear Svm

This paper presents a pedestrian detection method from a moving vehicle using optical flows and histogram of oriented gradients (HOG). A moving object is extracted from the relative motion by segmenting the region representing the same optical flows after compensating the egomotion of the camera. To obtain the optical flow, two consecutive images are divided into grid cells14×14pixels; then each cell is tracked in the current frame to find corresponding cell in the next frame. Using at least three corresponding cells, affine transformation is performed according to each corresponding cell in the consecutive images, so that conformed optical flows are extracted. The regions of moving object are detected as transformed objects, which are different from the previously registered background. Morphological process is applied to get the candidate human regions. In order to recognize the object, the HOG features are extracted on the candidate region and classified using linear support vector machine (SVM). The HOG feature vectors are used as input of linear SVM to classify the given input into pedestrian/nonpedestrian. The proposed method was tested in a moving vehicle and also confirmed through experiments using pedestrian dataset. It shows a significant improvement compared with original HOG using ETHZ pedestrian dataset.

scholarly journals Moving Object Localization Using Sound-Based Positioning System with Doppler Shift Compensation

Robotics ◽  
2013 ◽  
Vol 2 (2) ◽  
pp. 36-53 ◽  
Author(s):  
Slamet Widodo ◽  
Tomoo Shiigi ◽  
Naoki Hayashi ◽  
Hideo Kikuchi ◽  
Keigo Yanagida ◽  
...  
Keyword(s):  
Doppler Shift ◽  
Moving Object ◽  
Object Localization ◽  
Positioning System

Moving-Object-Localization Hardware Algorithm Employing OR-Amplification of Pixel Activities

2008 ◽  
Vol 47 (4) ◽  
pp. 2767-2773 ◽  
Author(s):  
Yusuke Niki ◽  
Yasuo Manzawa ◽  
Satoshi Kametani ◽  
Tadashi Shibata
Keyword(s):  
Moving Object ◽  
Object Localization
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