In the field of intelligent transportation system a great number of vision-based techniques have been proposed to prevent pedestrians from being hit by vehicles. This paper presents a system that can perform pedestrian and vehicle detection and monitoring of illegal activity in zebra crossings. In zebra crossing, according to the traffic light status, to fully avoid a collision, a driver or pedestrian should be warned earlier if they possess any illegal moves. In this research, at first, we detect the traffic light status of pedestrian and monitor the crossroad for vehicle pedestrian moves. The background subtraction based object detection and tracking is performed to detect pedestrian and vehicles in crossroads. Shadow removal, blob segmentation, trajectory analysis etc. are used to improve the object detection and classification performance. We demonstrate the experiment in several video sequences which are recorded in different time and environment such as day time and night time, sunny and raining environment. Our experimental results show that such simple and efficient technique can be used successfully as a traffic surveillance system to prevent accidents in zebra crossings.
This paper presents a method for contrast enhancement of medical images with preserving the local image details. The proposed method incorporates CLAHE and local image contrast preserving dynamic range compression. The method controls the amplification while preserving the local contrast of the image. The range of the gain parameter for local contrast enhancement varies from one image to another. The local contrast enhancement at any pixel position depends on the corresponding pixel neighborhood edge density. We have performed several experiments based on different image quality measures. Our proposed method provides more information about the image detail which affects the medical diagnosis. The experimental results by different image quality measures show that the output image quality of our proposed method is better than the CLAHE output.
In this paper, we present a fully automatic facial expression recognition system using support vector machines, with
geometric features extracted from the tracking of facial landmarks. Facial landmark initialization and tracking is
performed by using an elastic bunch graph matching algorithm. The facial expression recognition is performed based on
the features extracted from the tracking of not only individual landmarks, but also pair of landmarks. The recognition
accuracy on the Extended Kohn-Kanade (CK+) database shows that our proposed set of features produces better results,
because it utilizes time-varying graph information, as well as the motion of individual facial landmarks.