HoGG: Gabor and HoG-based human detection for surveillance in non-controlled environments

Neurocomputing

Volumn 100, Issue , 2013, Pages 19-30

  Conde, Cristina ^a   Moctezuma, Daniela ^a   Martín De Diego, Isaac ^a   Cabello, Enrique ^a  

^a UNIVERSIDAD REY JUAN CARLOS   (Spain)

Author keywords

Gabor filters; HoG; Human detection; Video surveillance

Indexed keywords

ALTERNATIVE ALGORITHMS; EXHAUSTIVE TESTS; FUTURE DESIGNS; HISTOGRAMS OF ORIENTED GRADIENTS; HOG; HUMAN DETECTION; INTELLIGENT SURVEILLANCE SYSTEMS; LOCAL FEATURE EXTRACTION; RESEARCH COMMUNITIES; STEERABLE FILTERS; VIDEO SURVEILLANCE;

FEATURE EXTRACTION; MONITORING; SECURITY SYSTEMS;

GABOR FILTERS;

ALGORITHM; ARTICLE; BIOLOGICAL MODEL; BODY BUILD; BODY POSTURE; CONTROLLED STUDY; DATA ANALYSIS; FACTUAL DATABASE; GABOR FILTER; HISTOGRAM OF ORIENTIED GRADIENT ALGORITHM; HUMAN; HUMAN COMPUTER INTERACTION; HUMAN DETECTION; IMAGE PROCESSING; INFORMATION PROCESSING; LIMIT OF DETECTION; PRIORITY JOURNAL; PROCESS OPTIMIZATION; TELEVISION CAMERA; VIDEORECORDING;

EID: 84868623992     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2011.12.037     Document Type: Article

References (39)

1. A. Ellis, A. Shahrokni, F. Ferryman, Overall evaluation of the PETS2009 results. 11th IEEE International Workshop on PETS, 2009, pp. 117-124.
2. K. Bhuvaneswari, H. Abdul Rauf, Edgelet based human detection and tracking by combined segmentation and soft decision, in: INCACEC 2009, June 2009, pp. 1-6.
3. A. Broggi, M. Bertozzi, A. Fascioli, M. Sechi, Shape-based pedestrian detection, in: IVS, 2000, pp. 215-220.
4. CAVIAR, CAVIAR Benchmark Data. Online: 〈〉, 2004. http://groups.inf.ed.ac.uk/vision/CAVIAR.
5. N. Dalal, B. Triggs, Histograms of oriented gradients for human detection, in: CVPR, 2005, pp. 886-893.
6. L.L. David Thirde, J. Ferryman, Overview of the PETS2006 challenge, in: 9th IEEE International Workshop on PETS, 2006, pp. 47-50.
7. M. Enzweiler, D. Gavrila, Monocular pedestrian detection: survey and experiments, Trans. Pattern Anal. Mach. Intell. December (2009) 2179-2195.
8. J. Ferryman, D. Tweed, Overview of the PETS2007 challenge, in: 10th IEEE International Workshop on PETS, 2007, pp. 49-53.
9. Freeman W.T., Adelson E.H. The design and use of steerable filters. IEEE Trans. Pattern Anal. Mach. Intell. 1991, 13(9):891-906.
10. D. Gavrila, Pedestrian detection from a moving vehicle, in: ECCV, 2000, pp. 37-49.
11. Gerónimo D., López A.M., Sappa A.D., Graf T. Survey of pedestrian detection for advanced driver assistance systems. Trans. Pattern Anal. Mach. Intell. 2010, 1239-1258.
12. Holte R.C. Very simple classification rules perform well on most commonly used datasets. Mach. Learn. 1993, 63-91.
13. Hu W., Tan T., Wang L., Maybank S. A survey on visual surveillance of object motion and behaviors. Trans. Syst. Man Cybern. 2004, 334-352.
14. INRIA, 2005, INRIA Person Dataset. Online: http://pascal.inrialpes.fr/data/human/.
15. Jiang W., Lam K.-M., Shen T. Efficient edge detection using simplified Gabor wavelets. IEEE Trans. Syst. Man Cybern. Part B 2009, 39(4):1036-1047.
16. W. Jiang, K.-M. Lam, T.-Z. Shen, Edge detection using simplified Gabor wavelets, in: 2008 International Conference on Neural Networks and Signal Processing, June 2008, pp. 586-591.
17. Krüger V., Sommer G. Gabor wavelet networks for efficient head pose estimation. Image Vision Comput. 2002, 20(9-10):665-672.
18. Kuncheva L., Rodríguez J. An experimental study on rotation forest ensembles. Multiple Classifier Syst. 2007, 459-468.
19. Kyrki V., Kamarainen J.-K., Kälviäinen H. Simple Gabor feature space for invariant object recognition. Pattern Recognition Lett. 2004, 25(3):311-318.
20. Lades M., Vorbrüggen J.C., Buhmann J.M., Lange J., von der Malsburg C., Würtz R.P., Konen W. Distortion invariant object recognition in the dynamic link architecture. IEEE Trans. Comput. 1993, 300-311.
21. I. Laptev, Improvements of object detection using boosted histograms, in: BMVC, 2006, pp. 949-958.
22. R. Lienhart, J. Maydt, An extended set of haar-like features for rapid object detection, in: ICIP, 2002, pp. 900-903.
23. Z. Lin, L.S. Davis, A pose-invariant descriptor for human detection and segmentation, in: ECCV, vol. 5305, 2008, pp. 423-436.
24. J. Liu, S. Ali, M. Shah, Recognizing human actions using multiple features, in: CVPR, 2008, pp. 1-8.
25. Lowe D.G. Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision 2004, 60(2):91-110.
26. J. Makhoul, F. Kubala, R. Schwartz, R. Weischedel, Performance measures for information extraction, in: DARPA Broadcast News Workshop, 1999, pp. 249-252.
27. V. Manohar, M. Boonstra, V. Korzhova, P. Soundararajan, D. Goldgof, R. Kasturi, S. Prasad, H. Raju, R. Bowers, J. Garofolo, PETS vs. VACE evaluation programs: a comparative study, in: 9th IEEE International Workshop on PETS, 2006.
28. Mikolajczyk K., Schmid C. A performance evaluation of local descriptors. IEEE Trans. Pattern Anal. Mach. Intell. 2005, 27(October (10)):1615-1630.
29. MIT, 2000, MIT pedestrian dataset. Online: http://cbcl.mit.edu/cbcl/software-datasets/PedestrianData.html.
30. Moeslund T.B., Hilton A., Krüger V. A survey of advances in vision-based human motion capture and analysis. Comput. Vision Image Understanding 2006, 90-126.
31. D.T. Nguyen, P. Ogunbona, W. Li, Human detection based on weighted template matching, in: ICME, 2009, pp. 634-637.
32. Polat E., Yeasin M., Sharma R. Robust tracking of human body parts for collaborative human computer interaction. Comput. Vision Image Understanding 2003, 44-69.
33. W.R. Schwartz, R. Gopalan, R. Chellappa, L.S. Davis, Robust human detection under occlusion by integrating face and person detectors, in: ICB, 2009, pp. 970-979.
34. E. Seemann, B. Leibe, K. Mikolajczyk, B. Schiele, An evaluation of local shape-based features for pedestrian detection, in: BMVC, 2005, pp. 11-20.
35. P.A. Viola, M.J. Jones, Rapid object detection using a boosted cascade of simple features, in: CVPR, 2001, pp. 511-518.
36. B. Wu, R. Nevatia, Detection of multiple, partially occluded humans in a single image by Bayesian combination of edgelet part detectors, in: ICCV, 2005, pp. 90-97.
37. X. Yuan, Z. Sun, Y.L. Varol, G. Bebis, A distributed visual surveillance system, in: AVSS, 2003, pp. 199-204.
38. T. Zhao, R. Nevatia, Bayesian human segmentation in crowded situations, in: CVPR, 2003, pp. 459-466.
39. Q. Zhu, M.-C. Yeh, K.-T. Cheng, S. Avidan, Fast human detection using a cascade of histograms of oriented gradients, in: CVPR, 2006, pp. 1491-1498.