Distributed Object Detection With Linear SVMs

In vision and learning, low computational complexity and high generalization are two important goals for video object detection. Low computational complexity here means not only fast speed but also less energy consumption. The sliding window object detection method with linear support vector machine...

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Bibliographic Details
Published in:IEEE transactions on cybernetics 2014-11, Vol.44 (11), p.2122-2133
Main Authors: Pang, Yanwei, Zhang, Kun, Yuan, Yuan, Wang, Kongqiao
Format: Article
Language:English
Subjects:
Algorithms
Cell-based histograms of oriented gradients (CHOG)
Classification
Complexity
Computation
Computational efficiency
Computer Simulation
computer vision
Feature extraction
Frames
Histograms
Humans
Image Enhancement - methods
Image Interpretation, Computer-Assisted - methods
linear classifier
Linear Models
machine learning
Mathematical analysis
Object detection
Pattern Recognition, Automated - methods
Photography - methods
Sensitivity and Specificity
Subtraction Technique
Support Vector Machine
Support vector machines
US Department of Defense
Vectors
Vectors (mathematics)
Video Recording - methods
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Summary:In vision and learning, low computational complexity and high generalization are two important goals for video object detection. Low computational complexity here means not only fast speed but also less energy consumption. The sliding window object detection method with linear support vector machines (SVMs) is a general object detection framework. The computational cost is herein mainly paid in complex feature extraction and innerproduct-based classification. This paper first develops a distributed object detection framework (DOD) by making the best use of spatial-temporal correlation, where the process of feature extraction and classification is distributed in the current frame and several previous frames. In each framework, only subfeature vectors are extracted and the response of partial linear classifier (i.e., subdecision value) is computed. To reduce the dimension of traditional block-based histograms of oriented gradients (BHOG) feature vector, this paper proposes a cell-based HOG (CHOG) algorithm, where the features in one cell are not shared with overlapping blocks. Using CHOG as feature descriptor, we develop CHOG-DOD as an instance of DOD framework. Experimental results on detection of hand, face, and pedestrian in video show the superiority of the proposed method.
ISSN:2168-2267
2168-2275
DOI:10.1109/TCYB.2014.2301453