Pedestrian detection in the embedded system, such as video surveillance equipment, usually involves low-resolution pedestrian samples and requires a low computational cost. Many pedestrian detectors rely on a large feature pool and suffer in their efficiency and performance for real-time monitoring. In this paper, a set of light-weight features is proposed to enhance the pedestrian detection performance when a small-medium scale of training data with low-resolution images is available. To address this issue, a difference matrix projection (DMP) is developed to compute aggregated multi-oriented pixel differences using global matrix operations. Both the pixel differences and aggregation are computed using global matrix projection to avoid the laborious iterative operations. We tested our method on the INRIA, Daimler Chrysler classification (Daimler-CB), NICTA, and Caltech Pedestrian datasets. The experiments on these benchmark data sets show encouraging results in terms of detection performance, particularly for image datasets with low-resolution pedestrians.
|Number of pages||14|
|Journal||IEEE Transactions on Intelligent Transportation Systems|
|Publication status||Published - 2020 Apr|
Bibliographical noteFunding Information:
Manuscript received December 17, 2017; revised August 6, 2018 and February 4, 2019; accepted March 31, 2019. Date of publication May 1, 2019; date of current version March 27, 2020. This work was supported by the National Research Foundation of Korea (NRF) through the Basic Science Research Program funded by the Ministry of Education, Science and Technology under Grant NRF-2018R1D1A1A09081956. The Associate Editor for this paper was K. Wang. (Corresponding author: Kar-Ann Toh.) X. Liu is with HP Labs, Palo Alto, CA 94304 USA (e-mail: email@example.com).
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All Science Journal Classification (ASJC) codes
- Automotive Engineering
- Mechanical Engineering
- Computer Science Applications