Journal of Scientific and Industrial Research

Open Access Open Access  Restricted Access Subscription Access

Object Sub-Categorization and Common Framework Method Using Iterative AdaBoost for Rapid Detection of Multiple Objects


Affiliations
1 School of Computing, Mohan Babu University, Tirupati 517 102, Tamil Nadu, India
2 Department of Information Technology, Sri Sairam Engineering College, Chennai 600 044, Tamil Nadu, India
3 School of Computer Science and Engineering (SCOPE), VIT-AP University, Amaravati 522 237, Andhra Pradesh, India
4 Department of Computer Science and Engineering, Amrita School of Computing, Amrita Vishwa Vidayapeetham Chennai 601 103, Tamil Nadu, India
5 School of Information Technology and Engineering, Vellore Institute of Technology, Vellore 632 014, Tamil Nadu, India
 

Object detection and tracking in real time has numerous applications and benefits in various fields like survey, crime detection etc. The idea of gaining useful information from real time scenes on the roads is called as Traffic Scene Perception (TSP). TSP actually consists of three subtasks namely, detecting things of interest, recognizing the discovered objects and tracking of the moving objects. Normally the results obtained could be of value in object recognition and tracking, however the detection of a particular object of interest is of higher value in any real time scenario. The prevalent systems focus on developing unique detectors for each of the above-mentioned subtasks and they work upon utilizing different features. This obviously is time consuming and involves multiple redundant operations. Hence in this paper a common framework using the enhanced AdaBoost algorithm is proposed which will examine all dense characteristics only once thereby increasing the detection speed substantially. An object sub-categorization strategy is proposed to capture the intra-class variance of objects in order to boost generalisation performance even more. We use three detection applications to demonstrate the efficiency of the proposed framework: traffic sign detection, car detection, and bike detection. On numerous benchmark data sets, the proposed framework delivers competitive performance using state-of-the-art techniques.

Keywords

Computer Vision, Segmentation, Supervised Learning, Traffic Scene Perception.
User
Notifications
Font Size

  • Agarwal S, Awan A & Roth D, Learning to detect objects in images via a sparse, part-based representation, IEEE Trans Pattern Anal Mach Intell, 26(11) (2004) 1475–1490.

  • Kyo S, Koga T, Sakurai K & Okazaki S, A robust vehicle detecting and tracking system for wet weather conditions using the IMAP-vision image processing board, in Proc IEEE Int Conf Intel Transport Syst (Cat. No.99TH8383) 1999, 423–428, doi: 10.1109/ITSC.1999.821095.

  • Dalal N & Triggs B, Histograms of oriented gradients for human detection, in Proc. IEEE Comput Soc Conf Comput Vis Pattern Recognit, (CVPR'05) 1, 2005, 886–893, doi: 10.1109/CVPR.2005.177

  • Viola P & Jones M J, Robust real-time face detection, Int J Comput Vis, 57(2) (2004) 137–154.

  • Felzenszwalb P F, Girshick R B, McAllester D A & Ramanan D, Object detection with discriminatively trained part-based models, IEEE Trans Pattern Anal Mach Intell, 32(9) (2009) 1627–1645.

  • Geiger A, Wojek C & Urtasun R, Joint 3D estimation of objects and scene layout, in Proc Adv Neural Inf Process Syst, 24 (2011).

  • Ahonen T, Hadid A & Pietikainen M, Face description with local binary patterns: application to face recognition, IEEE Trans Pattern Anal Mach Intell, 28(12) (2006) 2037–2041.

  • Wu J, Charles B S, Mullin M D & Rehg J M, Fast asymmetric learning for cascade face detection, IEEE Trans Pattern Anal Mach Intell, 30(3) (2008) 369–382.

  • Markus M, Radu T, Rodrigo B & Gool L V, Traffic sign recognition-how far are we from the solution, in Proc Int Jt Conf Neural Netw (IJCNN) 2013, 1–8.

  • Alberto B, Andrea C, Stefano C & Paolo Z, Lateral vehicles detection using monocular high resolution cameras on terramax, in Proc IEEE Intell Veh Symp 2008, 1143–1148.

  • De la Escalera A, Moreno L E, Salichs M A & Armingol J M, Road traffic sign detection and classification, IEEE Trans Ind Electron, 44(6) (1997) 848–859.

  • Jianzhu C, Fuqiang L, Zhi-peng L & Zhen J, Vehicle localisation using a single camera, in Proc IEEE Intel Veh Symp 2010, 871–876.

  • Caraffi C & Cattani S, VisLab at the Grand Challenge, IEEE Computer, 39(12) (2006) 36–37.

  • Nagarajan B & Devendran V, Vehicle classification under cluttered background and mild occlusion using Zernike features, Procedia Eng, 30 (2012) 201–209.

  • Houben S, Stallkamp J, Salmen J, Schlipsing M & Christian I, Detection of traffic signs in real-world images: the German traffic sign detection benchmark, Int Jt Conf Neural Netw, 2013, 1–8, DOI:10.1109/IJCNN.2013.6706807.

  • Geiger A, Lenz P & Urtasun R, Are we ready for autonomous driving?, the kittivision benchmark suite, Conf Comput Vis Pattern Recognit (IEEE) 2012, 3354–3361.

  • Qichang H, Paisitkriangkrai S, Chunhua S, Hengel A V D & Fatih P, Fast detection of multiple objects in traffic scenes with a common detection framework, IEEE Trans Intell Trans Syst, 17(4) (2016) 1002–1014, doi: 10.1109/TITS.2015.2496795.


Abstract Views: 77

PDF Views: 70




  • Object Sub-Categorization and Common Framework Method Using Iterative AdaBoost for Rapid Detection of Multiple Objects

Abstract Views: 77  |  PDF Views: 70

Authors

B Narendra Kumar Rao
School of Computing, Mohan Babu University, Tirupati 517 102, Tamil Nadu, India
R Ranjana
Department of Information Technology, Sri Sairam Engineering College, Chennai 600 044, Tamil Nadu, India
Nagendra Panini Challa
School of Computer Science and Engineering (SCOPE), VIT-AP University, Amaravati 522 237, Andhra Pradesh, India
S Sreenivasa Chakravarthi
Department of Computer Science and Engineering, Amrita School of Computing, Amrita Vishwa Vidayapeetham Chennai 601 103, Tamil Nadu, India
J Vellingiri
School of Information Technology and Engineering, Vellore Institute of Technology, Vellore 632 014, Tamil Nadu, India

Abstract


Object detection and tracking in real time has numerous applications and benefits in various fields like survey, crime detection etc. The idea of gaining useful information from real time scenes on the roads is called as Traffic Scene Perception (TSP). TSP actually consists of three subtasks namely, detecting things of interest, recognizing the discovered objects and tracking of the moving objects. Normally the results obtained could be of value in object recognition and tracking, however the detection of a particular object of interest is of higher value in any real time scenario. The prevalent systems focus on developing unique detectors for each of the above-mentioned subtasks and they work upon utilizing different features. This obviously is time consuming and involves multiple redundant operations. Hence in this paper a common framework using the enhanced AdaBoost algorithm is proposed which will examine all dense characteristics only once thereby increasing the detection speed substantially. An object sub-categorization strategy is proposed to capture the intra-class variance of objects in order to boost generalisation performance even more. We use three detection applications to demonstrate the efficiency of the proposed framework: traffic sign detection, car detection, and bike detection. On numerous benchmark data sets, the proposed framework delivers competitive performance using state-of-the-art techniques.

Keywords


Computer Vision, Segmentation, Supervised Learning, Traffic Scene Perception.

References