Multi-Lane Detection and Road Traffic Congestion Classification for Intelligent Transportation System

Tsai, Li-Wu; Chean, Yee-Choy; Ho, Chien-Peng; Gu, Hui-Zhen; Lee, Suh-Yin

doi:10.1016/j.egypro.2011.11.460

Cited by 1 publication

(2 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the application of vision-based traffic surveillance tools, many methods have been implemented, including adaptive median filters (3), color median (4), frame differentiation (5), mixture of Gaussians (6, 7), wavelet differentiation (8), kernel-based density estimation (9, 10) and sigma-delta filters (11). Also, mixture of Gaussians is implemented with shadow elimination based on color reflectance and gradient feature (7). Recently, a sigma-delta filter was proposed to continuously recognize a stopped vehicle as the foreground object and reduce the computational cost (11).…”

Section: Literature Reviewmentioning

confidence: 99%

“…The true occluded vehicles typically seem to reduce in scaling factor gradually and steadily while retaining their average speed fairly constant. Thus, additional processing was added to sort out true occlusion by reinforcing the algorithm with acceleration data: a a t (7) avg, th < where a avg,t is the average acceleration of a vehicle at a particular time t and a th is the threshold acceleration of 43 ft/s 2 . The acceleration data are highly unreliable without additional smoothing because of varying frame rates; however, they serve effectively in comparing the result produced from the first occlusion handling based on the scale factor.…”

Section: Occlusion Handlingmentioning

confidence: 99%

See 1 more Smart Citation

Full-Body Occlusion Handling and Density Analysis in Traffic Video-Surveillance Systems

Palinginis

Park

Kamiya

et al. 2014

Transportation Research Record

View full text Add to dashboard Cite

Vision-based traffic surveillance systems are among the most reliable, inexpensive, and highly applicable methodologies for surveying traffic conditions. The implementation of these strategies, however, is limited under certain conditions, such as the presence of vehicle occlusions or poor illumination conditions that lead to either overcounted or under-counted traffic data. The proposed motion-based methodology is intended to overcome these limitations by using a new technique for full-body occlusion handling of vehicles. The methodology is based on five main steps: calibration, detection, tracking, counting, and occlusion handling. The proposed methodology was tested with various 30–min videos and 452 preidentified cases of occlusion. Preliminary results indicated that the proposed methodology was reliable and robust in providing traffic density analysis. Future work may rely on the extension of the proposed methodology to deal with the detection of vehicles moving toward multiple directions.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Section: Occlusion Handlingmentioning

confidence: 99%