Due to increased adoption of digital inclusion in various businesses, location based services are gaining importance to provide value-added services for their customers. In this work, we present a computer vision based system for tracking customer locations by recognizing individual shopping carts inside shopping malls in order to facilitate location based services. We provide an efficient approach for cart recognition that consists of two stages: cart detection and then cart recognition. A binary pattern is placed between two pre-defined color markers and attached to each cart for recognition. The system takes live video feed as input from the cameras mounted on the aisles of the shopping mall and processes frames in realtime. In the cart detection stage, color segmentation, feature extraction and classification are used for detection of binary pattern along with color markers. In recognition stage, segmented binary strip is processed using spatial image processing techniques to decode the cart identification number.

We present an efficient algorithm for on-road vehicle (e.g. side and rear view of cars) detection problem using cascade of boosted classifiers. Adaptive boosting based classifier in cascaded structure is one of the few good approaches for object detection. This approach filters different non-target (negative) samples in different stages of cascaded structure according to their level of similarity with target object class. The boosted weak learners are quick and efficient for initial stages only, but in later stage of cascaded structure they are not efficient enough to remove the critical false alarms. In this paper, we propose a method of cascading complex features at the later stage of cascaded classifier to enhance the detection performance. We compared the performance of local and global texture features in combination with boosted haar like features. The best performance for on-road obstacle detection is achieved by Adaboost with Haar-like feature along with SVM and Histograms of Oriented Gradients (HOG) features.

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Traffic congestion problem is rising day-by-day due to increasing number of small to heavy weight vehicles on the road, poorly designed infrastructure, and ineffective control systems. This chapter addresses the problem of estimating computer vision based traffic density using video stream mining. We present an efficient approach for traffic density estimation using texture analysis along with Support Vector Machine (SVM) classifier, and describe analyzing traffic density for on-road traffic congestion control with better flow management. This approach facilitates integrated environment for users to derive traffic status by mining the available video streams from multiple cameras. It also facilitates processing video frames received from video cameras installed in traffic posts and classifies the frames according to traffic content at any particular instance. Time series information available from various input streams is combined with traffic video classification results to discover traffic trends.

Traffic congestion problem is rising day-by-day due to increasing number of small to heavy weight vehicles on the road, poorly designed infrastructure, and ineffective control systems. This chapter addresses the problem of estimating computer vision based traffic density using video stream mining. We present an efficient approach for traffic density estimation using texture analysis along with Support Vector Machine (SVM) classifier, and describe analyzing traffic density for on-road traffic congestion control with better flow management. This approach facilitates integrated environment for users to derive traffic status by mining the available video streams from multiple cameras. It also facilitates processing video frames received from video cameras installed in traffic posts and classifies the frames according to traffic content at any particular instance. Time series information available from various input streams is combined with traffic video classification results to discover traffic trends.