A hybrid object detection technique from dynamic background using Gaussian mixture models
- Authors: Haque, Mohammad , Murshed, Manzur , Paul, Manoranjan
- Date: 2008
- Type: Text , Conference paper
- Relation: 2008 IEEE 10th Workshop on Multimedia Signal Processing p. 915-920
- Full Text: false
- Reviewed:
- Description: Adaptive background modelling based object detection techniques are widely used in machine vision applications for handling the challenges of real-world multimodal background. But they are constrained to specific environment due to relying on environment specific parameters, and their performances also fluctuate across different operating speeds. On the other side, basic background subtraction (BBS) is not suitable for real applications due to manual background initialization requirement and its inability to handle repetitive multimodal background. However, it shows better stability across different operating speeds and can better eliminate noise, shadow, and trailing effect than adaptive techniques as no model adaptability or environment related parameters are involved. In this paper, we propose a hybrid object detection technique for incorporating the strengths of both approaches. In our technique, Gaussian mixture models (GMM) is used for maintaining an adaptive background model and both probabilistic and basic subtraction decisions are utilized for calculating inexpensive neighbourhood statistics for guiding the final object detection decision. Experimental results with two benchmark datasets and comparative analysis with recent adaptive object detection technique show the strength of the proposed technique in eliminating noise, shadow, and trailing effect while maintaining better stability across variable operating speeds.
Improved Gaussian mixtures for robust object detection by adaptive multi-background generation
- Authors: Haque, Mohammad , Murshed, Manzur , Paul, Manoranjan
- Date: 2008
- Type: Text , Conference paper
- Relation: 19th International Conference on Pattern Recognition p. 1-4
- Full Text: false
- Reviewed:
- Description: Adaptive Gaussian mixtures are widely used to model the dynamic background for real-time object detection. Recently the convergence speed of this approach is improved and a relatively robust statistical framework is proposed by Lee (PAMI, 2005). However, object quality still remains unacceptable due to poor Gaussian mixture quality, susceptibility to background/foreground data proportion, and inability to handle intrinsic background motion. This paper proposes an effective technique to eliminate these drawbacks by modifying the new model induction logic and using intensity difference thresholding to detect objects from one or more believe-to-be backgrounds. Experimental results on two benchmark datasets confirm that the object quality of the proposed technique is superior to that of Leepsilas technique at any model learning rate.
On stable dynamic background generation technique using Gaussian mixture models for robust object detection
- Authors: Haque, Mohammad , Murshed, Manzur , Paul, Manoranjan
- Date: 2008
- Type: Text , Conference paper
- Relation: 2008 IEEE Fifth International Conference on Advanced Video and Signal Based Surveillance p. 41-48
- Full Text: false
- Reviewed:
- Description: Gaussian mixture models (GMM) is used to represent the dynamic background in a surveillance video to detect the moving objects automatically. All the existing GMM based techniques inherently use the proportion by which a pixel is going to observe the background in any operating environment. In this paper we first show that such a proportion not only varies widely across different scenarios but also forbids using very fast learning rate. We then propose a dynamic background generation technique in conjunction with basic background subtraction which detected moving objects with improved stability and superior detection quality on a wide range of operating environments in two sets of benchmark surveillance sequences.
Panic-driven event detection from surveillance video stream without track and motion features
- Authors: Haque, Mohammad , Murshed, Manzur
- Date: 2010
- Type: Text , Conference paper
- Relation: 2010 IEEE International Conference on Multimedia & Expo p. 173-178
- Full Text: false
- Reviewed:
- Description: Modern surveillance systems are becoming highly automated in terms of scene understanding and event detection capabilities, and most existing methods rely on track-and motion-based features for event classification and anomaly detection. However, trajectory-based methods fail in public scenarios due to frequently loosing the object tracks, while the capabilities of motion-based methods are limited in detection of direction and velocity related anomalies. In this paper, a novel feature extraction and event detection method is presented without using any track and motion features where event discriminating characteristics are discovered from the dynamics of multiple temporal features extracted from foreground blobs and then confined in support vector machine based models for real-time event detection. Experimental results on benchmark datasets show that the proposed method can successfully discriminate panic-driven events like sudden split, runaway, and fighting from usual events.