視頻編碼參數(shù)對目標識別性能影響的研究

吳澤民; 劉濤; 姜青竹; 胡磊

doi:10.11999/JEIT141613

視頻編碼參數(shù)對目標識別性能影響的研究

doi: 10.11999/JEIT141613

基金項目:

航空科學基金(18265)

計量
- 文章訪問數(shù): 1499
- HTML全文瀏覽量: 131
- PDF下載量: 933
- 被引次數(shù): 0
出版歷程
- 收稿日期: 2014-12-18
- 修回日期: 2015-01-22
- 刊出日期: 2015-08-19

Video Coding Parameters Effect on Object Recognition

摘要

摘要: 國內(nèi)外研究人員對圖像目標分類識別和視頻編碼傳輸問題都分別進行了大量研究，但是對于視頻編碼參數(shù)對目標識別性能影響的定量關(guān)系，還沒有公開的文獻報導。針對這一問題，該文選擇典型的目標識別算法可變部件模型(DPM)和最常用的視頻編碼方法H.264/AVC作用測試對象，通過設計的編碼和檢測實驗，研究了碼率和分辨率參數(shù)對視頻目標識別性能的影響，并擬合了識別性能隨碼率和分辨率變化的函數(shù)關(guān)系。通過選取編碼器合適的碼率和分辨率工作參數(shù)，可以獲得信道帶寬與視頻目標識別性能的折中，為設計不同視頻應用的編碼優(yōu)化目標函數(shù)提供了依據(jù)。
- 計算機視覺 /
- 目標識別 /
- 視頻編碼 /
- 碼率 /
- 分辨率
Abstract: Researchers have done a great number of studies on the object recognition and the video coding transmission respectively. However, there are still no public reports about the influence on the object recognition raised by the video encoding parameters. For this issue, the Deformable Part Model (DPM), a typical object recognition algorithm and the most commonly-used video encoding methods-H.264/AVC are chosen as the test objects. In order to study how the code rates and the resolution affect the performance of video object recognition, the coding and detection experiments are designed and the function of recognition performance changes caused by the code rates and the resolution is fitted. The result shows that the compromise can be achieved between the channel bandwidth and the video object recognition performance through selecting the appropriate the code rates and the resolution parameters for the encoder which provides basis for encoding optimization object function of different video applications.
- Computer vision /
- Object recognition /
- Video code /
- Code rates /
- Resolution

HTML全文

參考文獻(23)

Li L J and Li F F. What, where and who? classifying events by scene and object recognition[C]. Proceedings of the IEEE 11th International Conference on Computer Vision, Rio de Janeiro, Brazil, 2007: 1-8.

Lei B, Wang T, Chen S, et al.. Object recognition based on adapative bag of feature and discriminative learning[C]. Proceedings of the 20th IEEE International Conference on Image Processing, Melbourne, Australia, 2013: 3390-3393.

Dalal N and Triggs B. Histograms of oriented gradients for human detection[C]. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Diego, USA, 2005, 1: 886-893.

Wei D, Zhao Y, Cheng R, et al.. An enhanced histogram of oriented gradient for pedestrian detection[C]. Proceedings of the 4th IEEE International Conference on Intelligent Control and Information Processing, Beijing, China, 2013: 459-463.

Felzenszwalb P F, Girshick R B, McAllester D, et al.. Object detection with discriminatively trained part-based models[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010, 32(9): 1627-1645.

Ding Y, Zhang J, Li J, et al.. A bag-of-feature model for video semantic annotation[C]. Proceedings of the 6th IEEE International Conference on Image and Graphics, Hefei, China, 2011: 696-701.

Huang D K, Chen K Y, and Cheng S C. Video object detection by model-based tracking[C]. Proceedings of the 20th IEEE International Symposium on Circuits and Systems, Beijing, China, 2013: 2384-2387.

Blair C, Robertson N M, and Hume D. Characterizing a heterogeneous system for person detection in video using histograms of oriented gradients: power versus speed versus accuracy[J]. IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 2013, 3(2): 236-247.

Liu Y, Jang Y, Woo W, et al.. Video-based object recognition using novel set-of-sets representations[C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Columbus, USA, 2014: 533-540.

Sharma P, Huang C, and Nevatia R. Unsupervised incremental learning for improved object detection in a video[C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Providence, USA, 2012: 3298-3305.

Wu Q and Li H. Mode dependent down-sampling and interpolation scheme for high efficiency video coding[J]. Signal Processing: Image Communication, 2013, 28(6): 581-596.

Wang T, Chen Y, He Y, et al.. A real-time rate control scheme and hardware implementation for H. 264/AVC

encoders[C]. Proceedings of the 5th IEEE International Congress on Image and Signal Processing, Chongqing, China, 2012: 5-9.

Felzenszwalb P F and Huttenlocher D P. Pictorial structures for object recognition[J]. International Journal of Computer Vision, 2005, 61(1): 55-79.

Felzenszwalb P F, Girshick R B, and McAllester D. Cascade object detection with deformable part models[C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, San Francisco, USA, 2010: 2241-2248.

Girshick R B, Felzenszwalb P F, and Mcallester D A. Object detection with grammar models[C]. Proceedings of the 25th IEEE Conference on Advances in Neural Information Processing Systems, Granada, Spain, 2011: 442-450.

袁武, 林守勛, 牛振東, 等. H. 264/AVC 碼率控制優(yōu)化算法[J]. 計算機學報, 2008, 31(2): 329-339.

Yuan W, Lin S X, Niu Z D, et al.. Efficient rate control schemes for H.264/AVC[J]. Chinese Journal of Computers, 2008, 31(2): 329-339.

魏江, 劉迪. 基于DM642的X.264編碼器優(yōu)化[J]. 現(xiàn)代電子技術(shù), 2011, 34(14): 68-70.

Wei J and Liu D. Optimization of X.264 encoder based on DM642 platform[J]. Modern Electronics Technique, 2011, 34(14): 68-70.

Huang Y H, Ou T S, and Su P Y. Perceptual rate distortion optimization using structural similarity index as quality metric[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2010, 20(11): 16141624.

Ou T S, Huang Y H, and Chen H H. SSIM-based perceptual rate control for video coding[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2011, 21(5): 682691.

Wang R, Huang C, and Chang P. Adaptive downsampling video coding with spatially scalable rate-distortion modeling [J]. IEEE Transactions on Circuits and Systems for Video Technology, 2014, 24(11): 1957-1968.

相關(guān)文章

施引文獻

資源附件(0)

訪問統(tǒng)計