Real-time traffic parameter extraction using entropy

Hsu, Wei-Lieh; Liao, H.-Y.M.; Jeng, Bor-Shenn; Fan, Kuo-Chin

doi:10.1049/ip-vis:20040314

Cited by 42 publications

(14 citation statements)

References 9 publications

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“…For vehicles' detection, most methods assume that the camera is static and then desired vehicles can be detected by the use of image differencing [16] [17]. The authors of [16] proposed a region-based approach to track and classify vehicles.…”

Section: Introductionmentioning

confidence: 99%

Dynamic background modeling for a safe road design

Doulamis

2010

Proceedings of the 3rd International Conference on PErvasive Technologies Related to Assistive Environments

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In this paper we propose a new dynamic algorithm able to automatically identify the background from images under severe changes of the environmental conditions. The algorithm exploits motion estimation techniques and background subtraction methods. The proposed scheme is implemented in road construction application scenarios under outdoor environments in which the camera vision is dramatically varied with respect to the weather conditions. Despite such variations, the background can be automatically detected improving the tracking performance for the vehicles and just assisting survey engineers in defining possible errors in the design yielding as deviations between the actual and the ideal trajectory of the vehicle over a turn. Experimental results in real-life road surveillance systems reveal the efficiency of the proposed scheme regardless of significant background changes. In addition, the proposed algorithm requires low computational complexity.

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Section: Introductionmentioning

confidence: 99%

Dynamic background modeling for a safe road design

Doulamis

2010

Proceedings of the 3rd International Conference on PErvasive Technologies Related to Assistive Environments

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“…For example, Beymer et al [13] proposed a vehicle tracking algorithm to estimate traffic parameters based on corner features. In addition, Liao et al [27] used entropy as an underlying measurement to calculate traffic flows and vehicle speeds. However, these approaches cannot further classify vehicles to more detailed types for providing more accurate information in traffic control.…”

Section: Introductionmentioning

confidence: 99%

“…For vehicle detection, most methods [6], [13], [27] assume that the camera is static and then desired vehicles can be detected by image differencing. Then, different tracking schemes like Kalman Filter [28] are designed to track each detected vehicle.…”

Section: Introductionmentioning

confidence: 99%

Automatic Traffic Surveillance System for Vehicle Tracking and Classification

Hsieh

Chen

et al. 2006

IEEE Trans. Intell. Transport. Syst.

375

134

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This paper presents an automatic traffic surveillance system to estimate important traffic parameters from video sequences using only one camera. Different from traditional methods which can classify vehicles to only cars and non-cars, the proposed method has a good capability to categorize vehicles into more specific classes by introducing a new "linearity" Once all features are extracted, an optimal classifier is then designed to robustly categorize vehicles into different classes. When recognizing a vehicle, the designed classifier can collect different evidences from its trajectories and the database to make the best decision for vehicle classification. Since more evidences are used, more robustness of classification can be achieved. Experimental results show that the proposed method is more robust, accurate, and powerful than other traditional methods, which utilize only vehicle size and single frame for classification.

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“…For vehicle detection, most methods assume that the camera is static and then desired vehicles can be detected by image differencing [8], [9]. In particular, in [9], entropy is used as an underlying measurement to calculate traffic flows and vehicle speeds, while in [8] a region-based approach is adopted to track and classify vehicles based on the establishment of correspondences between regions and vehicles. However, these methods are very sensitive to noise.…”

Section: Introductionmentioning

confidence: 99%

Angle spectrum for estimation of trajectory deviation using combined tracking and neural network labeling

Doulamis

Vescoukis

Georgopoulos

2008

Proceedings of the 1st ACM Workshop on Analysis and Retrieval of Events/Actions and Workflows in Video Streams

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In this paper, we combine rigid motion-based tracking algorithms and non-linear identification methods for automatic detecting and tracking vehicles' trajectory in roadways. In addition, we introduce the concept of the angle spectrum for determining the deviation of a vehicle trajectory from the ideal trace, provided by surveyor engineers. Motion-based tracking is implemented through frame differencing and advanced non-linear convolution filters such as the morphological opening by reconstruction. However, motion based tracking suffers from noise, occlusions and the fact that the detected moving region may contains more than one foreground objects (e.g., a vehicle approach another vehicle). For this reason, a neural network-based classification scheme is adopted in this paper for identifying foreground/background objects. The neural network models the colour and texture properties of the detected moving objects. Fusion algorithm are then exploited which it combine the output of the neural network classifier and the output of the motionbased tracking for efficiently detecting the vehicles trajectory. In the following, we introduce the concept of the angle spectrum which estimates the deviation between two curves, i.e., the vehicle trajectory and the ideal trace. The angle spectrum is computed through quantization of the polar coordinate space, adopted for the curve representation along with novel matching schemes. Experimental results are presented, which indicate the performance of the proposed method in real file environments.

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Real-time traffic parameter extraction using entropy

Cited by 42 publications

References 9 publications

Dynamic background modeling for a safe road design

Dynamic background modeling for a safe road design

Automatic Traffic Surveillance System for Vehicle Tracking and Classification

Angle spectrum for estimation of trajectory deviation using combined tracking and neural network labeling

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