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A Hybrid Object Detection Technique from Dynamic Background Using Gaussian Mixture Models Mahfuzul Haque, Manzur Murshed, and Manoranjan Paul Gippsland School of Information Technology, Monash University, Victoria 3842, Australia Email: {Mahfuzul.Haque, Manzur.Murshed, Manoranjan.Paul}@infotech.monash.edu.au The images shown in the header has been taken from http://www.informationliberation.com [1] Mahfuzul Haque, Manzur Murshed, and Manoranjan Paul, On Stable Dynamic Background Generation Technique using Gaussian Mixture Models for Robust Object Detection, IEEE International Conference On Advanced Video and Signal Based Surveillance (AVSS), New Mexico, USA, 2008. [2] Mahfuzul Haque, Manzur Murshed, and Manoranjan Paul, A Hybrid Object Detection Technique from Dynamic Background Using Gaussian Mixture Models, IEEE International Workshop on Multimedia Signal Processing (MMSP), Cairns, Australia, 2008. [3] Mahfuzul Haque, Manzur Murshed, and Manoranjan Paul, Improved Gaussian Mixtures for Robust Object Detection by Adaptive Multi-Background Generation, International Conference on Pattern Recognition (ICPR), Tampa, Florida, USA, 2008. The Complete System 8 Background Modelling Frame 1 Frame 2 Frame t .. K i t i t i t t i t X w X P 1 , , , ) , , ( ) ( ) ( ) ( 2 1 2 / 1 2 / , , 1 | | ) 2 ( 1 ) ( t t T t t X X n t t t e X Gaussian Mixture Model (GMM) for each pixel Input scenes A pixel model is constructed and updated for each pixel which maintains a mixture of Gaussian distributions for modelling multi-modal distribution caused by moving foregrounds and repetitive background motions [1-3]. 1 Abstract Adaptive background modelling is essential for robust object detection in real-world scenarios while basic background subtraction offers improved stability with varying operating environments [1]. This research presents a new technique by exploiting the strengths of both approaches, which not only reduces noise, shadow, and trailing effects, but also maintains superior stability across variable operating environments. P(x) Existing Models Intensity New Model 2 Qualitative Evaluation 7 Improved Model Quality by New Model Induction Scheme 3 Model Quality Visualisation 4 Hybrid Detection Algorithm 5 Red pixels correspond shadows and reflections detected with probabilistic subtraction, green pixels represent the low contrast regions detected using basic background subtraction, while yellow pixels are detected by both approaches. The hybrid algorithm utilises neighbourhood statistics of both decisions to minimise the red regions while maximising the green regions. Quantitative Evaluation 6 Single Model Double Models Many Models 0 127 255 First Frame Test Frame Ideal Result Lee’s Tech. Prop. Tech. Lee’s Tech. Prop. Tech. Comparisons of best error rates achieved by three techniques and standard deviations of error rates across three learning rates. Input Frames Visualisation Test Sequence S&G Lee Proposed S&G Lee Proposed 1. PETS2000 1.78 1.89 1.56 1.66 1.31 0.01 2. PETS2006-B1 5.50 8.22 3.89 3.20 1.18 0.52 3. PETS2006-B2 2.48 3.58 2.38 2.12 0.25 0.25 4. PETS2006-B3 2.46 3.42 2.36 2.66 1.08 0.31 5. PETS2006-B4 5.67 9.09 5.58 3.45 1.10 0.90 6. Bootstrap 11.83 13.26 11.80 1.89 2.14 1.26 7. Camouflage 13.01 25.46 10.08 10.99 9.64 2.32 8. Fground Aper. 16.31 52.39 15.74 21.65 7.39 0.03 9. LightSwitch 13.77 28.05 56.44 40.14 32.86 14.81 10. MovedObject 0.83 0.51 0.00 1.78 3.29 3.78 11. TimeOfDay 4.69 4.07 5.69 2.72 7.04 0.59 12. WavingTrees 13.68 18.38 12.91 1.79 0.45 0.09 13. Football 17.19 18.14 16.91 4.99 10.84 2.42 14. Walk 0.27 0.46 0.21 2.60 0.63 0.07 Error Rate (%) Stdev.
