Digest of Human Detection from CVPR2015
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The document summarizes recent research on human detection from the 2015 CVPR conference. It describes papers on features and models for human detection, including combination features using HOG, HOB, and HOC. It also discusses training detectors without real data using computer-generated scenes, and improving convolutional neural networks for detection. Benchmark datasets and methods detecting across visible and thermal spectra are also summarized. The document provides an overview of recent advances in computer vision for human detection.
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Digest of Human Detection from CVPR2015
- 1. Digest of Human Detection from CVPR 2015 Jan. 27th, 2016, Daichi SUZUO
- 2. Digest of Human Detection from CVPR2015 Features 1. Combination Features and Models for Human Detection - Y. Jiang et al. 2. Filtered Channel Features for Pedestrian Detection - S. Zhang et al. Training 3. Learning Scene-Specific Pedestrian Detectors without Real Data - H.Hattori et al. 4. Taking a Deeper Look at Pedestrians - J. Hosang et al. 5. Pedestrian Detection aided by Deep Learning Semantic Tasks - Y. Tian et al. Dataset / Benchmark 6. Multispectral Pedestrian Detection : Benchmark Dataset and Baseline - S. Hwang et al.
- 3. Fundamentals of Human Detection • Machine learning based bi-class classifier • Sliding window search Negative class Positive class Convert to image feature Training Classifier Classifier Crop Feature extraction Human? Not human?
- 4. Image features 1. Combination Features and Models for Human Detection - Y. Jiang et al. 2. Filtered Channel Features for Pedestrian Detection - S. Zhang et al.
- 5. θ 1. Combination Features and Models for Human Detection - Y. Jiang et al. • Popular HOG feature[Dalal05] Input image Edge-image Edge extraction (“cell”) pixel-wise gradient power Histogram
- 6. θ • Popular HOG feature[Dalal05]: 1st order feature power Input image 1st derivative Differentiate Histogram (“cell”) pixel-wise gradient idea: How about extending to 0-th/2nd order? 1. Combination Features and Models for Human Detection - Y. Jiang et al.
- 7. 1. Combination Features and Models for Human Detection - Y. Jiang et al. • 2nd order: HOB – “bar” shape • Same as HOG, just using 2nd derivative • 0th order: HOC – color feature • Using HSI color space; H as θ, S as power ignore I convert to HSI R G V
- 8. 1. Combination Features and Models for Human Detection - Y. Jiang et al. • Combine them into one vector: HOG-III feature
- 9. 1. Combination Features and Models for Human Detection - Y. Jiang et al. • Train different classifiers from the same HOG-IIIs • Detect individually, and fuse into one result Input image HOG-III features Detection by Grammar model[Girshick11] Detection by Poselet model[Bourdev10] Fusion Final result (This is one of the key process of the method Please refer the original paper for more details)
- 10. 1. Combination Features and Models for Human Detection - Y. Jiang et al. Effect of HOG-III Effect of Fusion Feature AP HOG 45.8% HOC+HOG+HOB 50.1% HOG-III 51.3% Classifier AP Single use of Grammer 45.8% Single use of Poselet 47.0% Fusion 52.3% Combining HOG-III and Fusion performs best
- 11. 2. Filtered Channel Features for Pedestrian Detection - S. Zhang et al. • Extension of “Integral Channel Features” [Dollár09] • ChnFtrs: Extension of “Viola-Jones method” [Viola02] (Viola-Jones method) … … … Input image Learn decision-tree by AdaBoost Extract “Haar-like” Features (scalar) ※Sum of difference between white and black region
- 12. 2. Filtered Channel Features for Pedestrian Detection - S. Zhang et al. • Extension of “Integral Channel Features” [Dollár09] • ChnFtrs: Extension of “Viola-Jones method” [Viola02] (Integral Channel Features) … … … Input image Learn decision-tree by AdaBoost “channel” Extract sum of rectangle ※Unlike Haar-like Transform
- 13. 2. Filtered Channel Features for Pedestrian Detection - S. Zhang et al. • Extension of “Integral Channel Features” [Dollár09] • ChnFtrs: Extension of “Viola-Jones method” [Viola02] (Filtered Channel Features) … … … Learn decision-tree by AdaBoost “channel” Apply various filters (convolution) … * * Pick-up pixel value as a feature …
- 14. 2. Filtered Channel Features for Pedestrian Detection - S. Zhang et al. Using 50 filters performs bestAchieved the highest accuracy
- 15. Training 3. Learning Scene-Specific Pedestrian Detectors without Real Data - H. Hattori et al. 4. Taking a Deeper Look at Pedestrians - J. Hosang et al. 5. Pedestrian Detection aided by Deep Learning Semantic Tasks - Y. Tian et al.
- 16. • Train detector by CG-based training datasets 3. Learning Scene-Specific Pedestrian Detectors without Real Data - H. Hattori et al. Real background (static image) annotate CG-based human composite Simulated scene
- 17. • Not only scene-specific, but also location-specific! 3. Learning Scene-Specific Pedestrian Detectors without Real Data - H. Hattori et al. … Classifier Classifier … Grid with overwrap (102~105 patches) Training images (~103 pos, ~103 neg for each patch) Joint Classifier Ensemble Training Scene-specific Location-specific detectors
- 18. 3. Learning Scene-Specific Pedestrian Detectors without Real Data - H. Hattori et al. Patch size # detectors Avg. Precision 8x8 371 .802 16x16 102 .798 32x32 30 .764 Effect of location-specific detection Example of the detection result Comparison
- 19. “convnet still underperforms state-of-the-arts” …Really? Enhance know-how of convnet based detector 4. Taking a Deeper Look at Pedestrians - J. Hosang et al. • Small network (CifarNet) / Big network (AlexNet) • Window size • How to collect training images • Fine-tuning • Number and Type of layers • …
- 20. 4. Taking a Deeper Look at Pedestrians - J. Hosang et al. Convnet with the best configuration outperforms! Interesting points: • Ratio of pos/neg does not affect to the accuracy so much • Data-augumentation is effective • Network size should be chosen by the amount of training samples • ...
- 21. 5. Pedestrian Detection aided by Deep Learning Semantic Tasks - Y. Tian et al. Binary-classification is sometimes insufficient… Human Not human (Hard negatives) It is necessary to use semantic information jointly
- 22. 5. Pedestrian Detection aided by Deep Learning Semantic Tasks - Y. Tian et al. Classify pedestrian and Recognize semantic at once!
- 23. 5. Pedestrian Detection aided by Deep Learning Semantic Tasks - Y. Tian et al. Classify pedestrian and Recognize semantic at once! Also recognizes current scene semantics • Pedestrian attribute (e.g. wearing backpack) • Background attribute (e.g. road, sky, …)
- 24. 5. Pedestrian Detection aided by Deep Learning Semantic Tasks - Y. Tian et al. Classify pedestrian and Recognize semantic at once! Difficult to collect various (annotated) negs from one dataset… Transfer from other annotated datasets by TA-CNN (Please refer the original and related papers for more details about TA-CNN…)
- 25. 5. Pedestrian Detection aided by Deep Learning Semantic Tasks - Y. Tian et al. Comparison with CNN-based methods Example of detection results
- 26. Benchmark / Dataset 6. Multispectral Pedestrian Detection : Benchmark Dataset and Baseline - S. Hwang et al.
- 27. • Dataset of visible-light and thermal image 6. Multispectral Pedestrian Detection : Benchmark Dataset and Baseline - S. Hwang et al. Contributions: • Color and thermal images • Both test/training data • Temporally-corresponded tag • Large enough • …
- 28. Takeaways
- 29. • Human detection is still challenging • Deep learning does not necessarily solve every problems at this moment • There are several knowledge that might be helpful for your research/hobby/… Takeaways
- 30. References / Supplemental materials
- 31. 1. Filtered channel features for pedestrian detection 4. Taking a Deeper Look at Pedestrians • Author's website: http://rodrigob.github.io/ 3. Learning Scene-Specific Pedestrian Detectors without Real Data • Project: http://vishnu.boddeti.net/projects/detection-by-synthesis.html • YouTube: https://youtu.be/2Jf7faozHUs 5. Pedestrian Detection aided by Deep Learning Semantic Tasks • Project: http://mmlab.ie.cuhk.edu.hk/projects/TA-CNN/ 6. Multispectral Pedestrian Detection: Benchmark Dataset and Baseline • Lab: http://rcv.kaist.ac.kr/v2/ And all the papers of CVPR2015 are available at cv-foundation.org See also
Editor's Notes
- https://en.wikipedia.org/wiki/File:Lenna.png
- https://pixabay.com/en/newcastle-upon-tyne-england-road-594165/
- Then, let us go to the main topics. The first group is about image features, there are 2 papers. Why are features important? Good feature suppresses useless change of images, like lightning conditions, while keeping enough information. This is kind of a trade-off, where the difficulty comes from.
- The principle of good detector is; If the training dataset can be collected from exactly same scene as the detection time, the detector will surely outperform the general detector trained by common training data. But data-annotation is really heavy task, although it is necessary to create training dataset. The basic idea of the paper is generation of training data by combination of actual background and CG-generated people. This approach is called “generative learning”.
- https://twitter.com/hirokatukataoka/status/592874061170135040
- https://twitter.com/hirokatukataoka/status/592874061170135040 あと表2もはる。Location specificにするとよくなること。
- Then, let us go to the main topics. The first group is about image features, there are 2 papers. Why are features important? Good feature suppresses useless change of images, like lightning conditions, while keeping enough information. This is kind of a trade-off, where the difficulty comes from.
- ・色やサーマル単独だったらあるけど同時提供は初。などなど
- Then, let us go to the main topics. The first group is about image features, there are 2 papers. Why are features important? Good feature suppresses useless change of images, like lightning conditions, while keeping enough information. This is kind of a trade-off, where the difficulty comes from.
- ・色やサーマル単独だったらあるけど同時提供は初。などなど
- Then, let us go to the main topics. The first group is about image features, there are 2 papers. Why are features important? Good feature suppresses useless change of images, like lightning conditions, while keeping enough information. This is kind of a trade-off, where the difficulty comes from.