NDLI: Weight-loss control sampling for the training of boosted pedestrian detectors

Content Provider	IEEE Xplore Digital Library
Author	Chenxu Gao Shiming Lai Zhihui Xiong Maojun Zhang
Copyright Year	2014
Description	Author affiliation: Coll. of Inf. Syst. & Manage, Nat. Univ. of Defense Technol., Changsha, China (Chenxu Gao; Shiming Lai; Zhihui Xiong; Maojun Zhang)
Abstract	When we apply AdaBoost in pedestrian detection, a large number of examples are needed to train a detector. Except for designing features, a reasonable utilization of training examples is also significant to the detection accuracy and training time. In this paper, we propose a new method, named Weight-Loss Control Sampling (WLCS), to deal with the negative training examples by improving the training process of AdaBoost. The WLCS updates the negative training set by sampling hard examples from original negative set. It determines when to implement sampling process by the weight-loss of the negative training set. And we reduce the capacity of negatives after each sampling to accelerate the training. In this paper, we implement experiments to prove that the WLCS can train a better classifier in a short training time via a specific pedestrian detection method.
Starting Page	979
Ending Page	982
File Size	211484
Page Count	4
File Format	PDF
ISBN	9781479932788
ISSN	23270594
e-ISBN	9781479932795
DOI	10.1109/ICSESS.2014.6933729
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-06-27
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Object Detection Accuracy Detectors Object detection Feature extraction Boosting Acceleration Machine Learning Testing
Content Type	Text
Resource Type	Article

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