R. Aarthi

Vehicle Detection in Static Images Using Color and Corner Map

This paper presents an approach to identify the vehicle in the static images using color and corner map. The detection of vehicles in a traffic scene can address wide range of traffic problems. Here an attempt has been made to reduce the search time to find the possible vehicle candidates thereby reducing the computation time without a full search. A color transformation is used to project all the colors of input pixels to a new feature space such that vehicle pixels can be easily distinguished from non-vehicle ones. Bayesian classifier is adopted for verifying the vehicle pixels from the background. Corner map is used for removing the false detections and to verify the vehicle candidates.

A Study of Household Object Recognition Using SIFT-Based Bag-of-Words Dictionary and SVMs

In the era of computational intelligence, computer vision-based techniques for robotic cognition have gained prominence. One of the important problems in computer vision is the recognition of objects in real-time environments. In this paper, we construct a SIFT-based SVM classifier and analyze its performance for real-time object recognition. Ten household objects from the CALTECH-101 dataset are chosen, and the optimal train-test ratio is identified by keeping other SVM parameters constant. The system achieves an overall accuracy of 85 % by maintaining the ratio as 3:2. The difficulties faced in adapting such a classifier for real-time recognition are discussed.

A Survey of Different Stages for Monitoring Traffic Rule Violation

A traffic surveillance system is a controlled system that helps to monitor and regulate the traffic. In this paper, a method for extracting the license number of the vehicle that is exceeding the speed limit is proposed. A Study is conducted by covering various stages of monitoring system such as vehicle detection in the video, tracking the vehicle for speed calculation and extracting the vehicle number in the number plate that can be used in places with high public vicinity.

Saliency based modified chamfers matching method for sketch based image retrieval

Recent advances of tablet PC and multi-touch screen technology raised increasing interest on users search and retrieve the desired images in databases in a simple manner. Sketch based image retrieval (SBIR) emerged as a more expressive and interactive way to perform image search. Our focuses on this work is to enhance the methods of Indexable Oriented Chamfer Matching using salient feature detection algorithms. As per psychologist observation, edge is one of the most dominant feature of an image to represent the content. Prioritization is done based on edge content of image by ignoring parameters like color, texture. Experiments are done in benchmark dataset of [11] demonstrate the better performance of our approach.

A Generic Bio-inspired Framework for Detecting Humans Based on Saliency Detection

Even with all its advancement in technology, computer vision system cannot competes with nature’s gift—the brains, that arranges the objects quickly and extract the necessary information from huge data. A bio-inspired feature selection method is proposed for detecting the humans using saliency detection. It is performed by tuning prominent features such as color, orientation, and intensity in bottom-up approach to locate the probable candidate regions of humans in an image. Further, the results improved in detection phase that makes use of weights learned from training samples to ignore non-human regions in the candidate regions. The overall system has an accuracy rate of 90 % for detecting the human region.

Image Restoration Using Knowledge from the Image

There are various real world situations where, a portion of the image is lost or damaged which needs an image restoration. A Prior knowledge of the image may not be available for restoring the image, which demands for a knowledge derivation from the image itself. Restoring the lost portions of the image based on the knowledge obtained from the image area surrounding the lost area is called as Digital Image Inpainting. The information content in the lost area could contain structural information like edges or textural information like repeating patterns. This knowledge is derived from the boundary area surrounding the lost area. Based on this, the lost area is restored by looking at similar information in the same image. Experimentation have been done on various images and observed that the algorithm restores the image in a visually plausible way.

Automatic isolation and classification of vehicles in a traffic video

Among the diverse applications of computer and communication technologies, Intelligent Transport System aids in simplifying transport problems. Its aim is to gather data and provide timely feedback to traffic managers (traffic policemen) and road users. The various problems involved in processing real-time traffic data has been addressed in several areas of research that includes vehicle detection, tracking and classification. This paper proposes a technique for isolation and classification of vehicles at an abstract level. The isolation technique aims at locating regions of interest (vehicles) within the image to be classified. Classification is performed in two categories. The first category is to identify the predominant color and the second is to classify the vehicle as light or heavy. The experimental results show an accuracy of 82% even for traffic video sequences involving complicated scenes.