A method for vehicle count in the presence of multiple-vehicle occlusions in traffic images

This paper proposes a novel method for accurately counting the number of vehicles that are involved in multiple-vehicle occlusions, based on the resolvability of each occluded vehicle, as seen in a monocular traffic image sequence. Assuming that the occluded vehicles are segmented from the road background by a previously proposed vehicle segmentation method and that a deformable model is geometrically fitted onto the occluded vehicles, the proposed method first deduces the number of vertices per individual vehicle from the camera configuration. Second, a contour description model is utilized to describe the direction of the contour segments with respect to its vanishing points, from which individual contour description and vehicle count are determined. Third, it assigns a resolvability index to each occluded vehicle based on a resolvability model, from which each occluded vehicle model is resolved and the vehicle dimension is measured. The proposed method has been tested on 267 sets of real-world monocular traffic images containing 3074 vehicles with multiple-vehicle occlusions and is found to be 100% accurate in calculating vehicle count, in comparison with human inspection. By comparing the estimated dimensions of the resolved generalized deformable model of the vehicle with the actual dimensions published by the manufacturers, the root-mean-square error for width, length, and height estimations are found to be 48, 279, and 76 mm, respectively. © 2007 IEEE.published_or_final_versio

Multiscale space vehicle component identification

Vision based vehicle recognition systems have an important role in traffic surveillance. Most of these systems however fail to distinguish vehicles with similar dimensions due to the lack of other details. This paper presents a new scale space method for identifying components of moving vehicles to enable recognition eventually. In the proposed method, vehicles are first divided into multiscale regions based on the center of gravity of the foreground vehicle mask. It utilizes both the texture scale space and the intensity scale space to determine regions that are homogenous in texture and intensity, from which vehicle components are identified based on the relations between these regions. This method was tested on over a hundred outdoor traffic images and the results are very promising.published_or_final_versio

Highly accurate texture-based vehicle segmentation method

In modern traffic surveillance, computer vision methods have often been employed to detect vehicles of interest because of the rich information content contained in an image. Segmentation of moving vehicles using image processing and analysis algorithms has been an important research topic in the past decade. However, segmentation results are strongly affected by two issues: moving cast shadows and reflective regions, both of which reduce accuracy and require postprocessing to alleviate the degradation. We propose an efficient and highly accurate texture-based method for extracting the boundary of vehicles from the stationary background that is free from the effect of moving cast shadows and reflective regions. The segmentation method utilizes the differences in textural property between the road, vehicle cast shadow, reflection on the vehicle, and the vehicle itself, rather than just the intensity differences between them. By further combining the luminance and chrominance properties into an OR map, a number of foreground vehicle masks are constructed through a series of morphological operations, where each mask describes the outline of a moving vehicle. The proposed method has been tested on real-world traffic image sequences and achieved an average error rate of 3.44% for 50 tested vehicle images. © 2004 Society of Photo-Optical Instrumentation Engineers.published_or_final_versio

Vehicle-component identification based on multiscale textural couriers

This paper presents a novel method for identifying vehicle components in a monocular traffic image sequence. In the proposed method, the vehicles are first divided into multiscale regions based on the center of gravity of the foreground vehicle mask and the calibrated-camera parameters. With these multiscale regions, textural couriers are generated based on the localized variances of the foreground vehicle image. A new scale-space model is subsequently created based on the textural couriers to provide a topological structure of the vehicle. In this model, key feature points of the vehicle can significantly be described based on the topological structure to determine the regions that are homogenous in texture from which vehicle components can be identified by segmenting the key feature points. Since no motion information is required in order to segment the vehicles prior to recognition, the proposed system can be used in situations where extensive observation time is not available or motion information is unreliable. This novel method can be used in real-world systems such as vehicle-shape reconstruction, vehicle classification, and vehicle recognition. This method was demonstrated and tested on 200 different vehicle samples captured in routine outdoor traffic images and achieved an average error rate of 6.8% with a variety of vehicles and traffic scenes. © 2006 IEEE.published_or_final_versio

A novel method for handling vehicle occlusion in visual traffic surveillance

This paper presents a novel algorithm for handling occlusion in visual traffic surveillance (VTS) by geometrically splitting the model that has been fitted onto the composite binary vehicle mask of two occluded vehicles. The proposed algorithm consists of a critical points detection step, a critical points clustering step and a model partition step using the vanishing point of the road. The critical points detection step detects the major critical points on the contour of the binary vehicle mask. The critical points clustering step selects the best critical points among the detected critical points as the reference points for the model partition. The model partition step partitions the model by exploiting the information of the vanishing point of the road and the selected critical points. The proposed algorithm was tested on a number of real traffic image sequences, and has demonstrated that it can successfully partition the model that has been fitted onto two occluded vehicles. To evaluate the accuracy, the dimensions of each individual vehicle are estimated based on the partitioned model. The estimation accuracies in vehicle width, length and height are 95.5%, 93.4% and 97.7% respectively.published_or_final_versio

Comparison of post-treatment plasma EBV DNA with nasopharyngeal biopsy in patients after radical (chemo) radiotherapy for non-metatatic nasopharyngeal cancer

This journal suppl. entitled: Proceedings of the American Society for Radiation Oncology 56th Annual Meeting, ASTRO's 56th Annual Meeting ... 2014Oral Scientific SessionPURPOSE/OBJECTIVE(S): Random nasopharyngeal biopsy after completion of intensity-modulated radiation therapy (IMRT) for non-metastatic nasopharyngeal cancer (NPC) is routinely practiced in Hong Kong to confirm local remission. Plasma EBV DNA is proven an accurate marker for NPC. We carried out a prospective study comparing the correlation between post-IMRT nasopharyngeal biopsy and EBV DNA, to investigate if EBV DNA can substitute biopsy to confirm local remission. MATERIALS/METHODS: Patients with non-metastatic NPC treated with definitive (chemo) IMRT diagnosed between January 2011 and March 2013 were recruited. After baseline workup ...postprin

Optimising antimicrobial prescription in hospitals by introducing an antimicrobial stewardship programme in Hong Kong: Consensus statement

Objective. To discuss the implementation of an 'antimicrobial stewardship programme' as a means to improve the quality of antimicrobial use in a hospital setting in Hong Kong. Participants. Consensus working group on 'antimicrobial stewardship programme', The Scientific Committee on Infection Control, Centre for Health Protection, Department of Health, comprised 11 experts. The remit of the working group was to discuss the rationale and requirement for optimising antimicrobial prescriptions in hospitals by the introduction of an 'antimicrobial stewardship programme'. Evidence. PubMed articles, national and international guidelines, and abstracts of international meetings published between January 2000 and December 2004 on programmes for improving the use of antimicrobials in hospitals. Only English medical literature was reviewed. Consensus process. Data search was performed independently by three members of the working group. They met on three occasions before the meeting to discuss all collected articles. A final draft was circulated to the working group before a meeting on 3 January 2005. Five commonly asked questions about an 'antimicrobial stewardship programme' were selected for discussion by the participants. Published information on the rationale, components, outcome measures, advantages, and disadvantages of the programme was reviewed. Recent unpublished data from local studies of an 'antimicrobial stewardship programme' were also discussed. The timing, potential problems, and practical issues involved in the implementation of an 'antimicrobial stewardship programme' in Hong Kong were then considered. The consensus statement was circulated to and approved by all participants. Conclusion. The continuous indiscriminate and excessive use of antimicrobial agents promotes the emergence of antibiotic-resistant organisms. Antimicrobial resistance substantially raises already-rising health care costs and increases patient morbidity and mortality. Pattern of prescriptions in hospitals can be improved through the implementation of an 'antimicrobial stewardship programme'. A 'universal' and 'continuous' 'antimicrobial stewardship programme' should now be established in Hong Kong hospitals.published_or_final_versio

Multi-scale space vehicle component identification

Vision based vehicle recognition systems have an important role in traffic surveillance. Most of these systems however fail to distinguish vehicles with similar dimensions due to the lack of other details. This paper presents a new scale space method for identifying components of moving vehicles to enable recognition eventually. In the proposed method, vehicles are first divided into multi-scale regions based on the center of gravity of the foreground vehicle mask. It utilizes both the texture scale space and the intensity scale space to determine regions that are homogenous in texture and intensity, from which vehicle components are identified based on the relations between these regions. This method was tested on over a hundred outdoor traffic images and the results are very promising. ©2004 IEEE.link_to_subscribed_fulltex

A novel method for resolving vehicle occlusion in a monocular traffic-image sequence

This paper presents a novel method for resolving the occlusion of vehicles seen in a sequence of traffic images taken from a single roadside mounted camera. Its concept is built upon a previously proposed vehicle-segmentation method, which is able to extract the vehicle shape out of the background accurately without the effect of shadows and other visual artifacts. Based on the segmented shape and that the shape can be represented by a simple cubical model, we propose a two-step method: first, detect the curvature of the shape contour to generate a data set of the vehicles occluded and, second, decompose it into individual vehicle models using a vanishing point in three dimensions and the set of curvature points of the composite model. The proposed method has been tested on a number of monocular traffic-image sequences and found that it detects the presence of occlusion correctly and resolves most of the occlusion cases involving two vehicles. It only fails when the occlusion was very severe. Further analysis of vehicle dimension also shows that the average estimation accuracy for vehicle width, length, and height are 94.78%, 94.09%, and 95.44%, respectively.published_or_final_versio