車載視頻下改進的核相關濾波跟蹤算法
doi: 10.11999/JEIT171109
基金項目:
國家自然科學基金(61471124)，福建省重大重點科技項目(2017H6009, 2018H0018)，賽爾網(wǎng)絡創(chuàng)新項目(NGII20160208, NGII20170201)
Improved Kernel Correlation Filtering Tracking for Vehicle Video
Funds:
The National Natural Science Foundation of China (61471124), The Major Science and Technology Projects in Fujian Proviuce (2017H6009, 2018H0018), The Cernet Innovation Projects (NGII20160208, NGII20170201)
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摘要: 針對相關濾波跟蹤算法在車載視頻下由于環(huán)境復雜及目標尺度變化等情況下容易跟蹤失敗的問題，該文提出一種基于背景信息的尺度自適應相關濾波跟蹤算法。首先利用背景感知相關濾波跟蹤器融合方向梯度直方圖特征預測目標下一幀位置，然后根據(jù)預測位置選取圖像塊進行檢測，最后結(jié)合動態(tài)尺度比例金字塔模型對目標進行尺度估計。實驗選取了KITTI數(shù)據(jù)庫中23段車載視頻和標注國內(nèi)的4段車載視頻進行測試，實驗結(jié)果表明，該算法能有效降低車載環(huán)境的復雜背景、目標尺度變化等因素干擾，整體性能優(yōu)于KCF, DSST, SAMF, SATPLE等主流相關濾波算法，對車載環(huán)境下復雜背景和尺度變化的目標跟蹤具有魯棒性。Abstract: For videos captured by in-car cameras, the filter-based tracking is a challenging task due to complex environments and mutable object scales. A scale adaptive tracking filter is proposed based on the background information. Firstly, the relative motion of each object is estimated by extracting features from gradient histograms between frames. Then, the object location on the next frame is determined and utilized to delimit an image block. Finally, the object scale is obtained through dynamic scaling pyramid model within image block. The proposed algorithm is examined by 27 in-car videos including 23 KITTI videos and 4 domestic videos. In experiments, the proposed algorithm suppresses effectively the interferences of environments and objects. It achieves more accurate and more robust object tracking than several popular benchmarks including KCF, DSST, SAMF, SATPLE.
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