Theses and Dissertations

Permanent URI for this collectionhttp://ir.daiict.ac.in/handle/123456789/1

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Subject: search.filters.subject.Intelligent transportation systems

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    ItemOpen Access
    Road detection for intelligent transport systems
    (Dhirubhai Ambani Institute of Information and Communication Technology, 2015) Shah, Falak; Shah, Pratik; Dubey, Rahul
    Road detection is an important machine vision problem with applications to driver assistance systems and autonomous vehicles. We carried out a literature survey of the state of the art road detection algorithms. Simulations of these algorithms were performed on road images taken from multiple datasets which revealed certain limitations such as failure under shadows or in the absence of lane markers. This is why the past few years saw the emergence of illuminant invariant based road detection techniques as the state of the art. As the name suggests, illuminant invariant is a feature which contains the colour information of the surface being captured independent of the illumination source. However, the derivation of illuminant invariant image from the RGB image makes use of the assumption that the surface being captured is lambertian. The smooth road surfaces that reflect sunlight are specular and they violate the lambertian assumption. Thus, the algorithms based on illuminant invariant feature fail to detect the road region containing specularities. The road detection algorithm functions by building a road model in the illuminant invariant feature space for each frame. The white markings that are painted over the roads in the form of zebra crossings, lane markers and arrows are not included into the road model. Hence, the algorithm fails to detect them as a part of road region. The first contribution of this thesis is to address the limitations of specularities and lane markers, thus improving the robustness of the state of the art road detection algorithm. We propose a novel specularity detection and removal method for road scenes which also removes the white markings present in the road image. The region of the image containing specularities/ markers is filled with same shade as its surrounding region. Any road detection algorithm has two aspects- the first is robustness and next is real time implementation. The second contribution of this thesis is implementation of the proposed algorithm on BeagleBone Black and Rapsberry pi-2, which are low cost, low-power single-board computers. This provides a proof-of-concept of real time computation. Thus, the thesis improves the accuracy of the state of the art path detection and provides means of real time implementation on mobile platforms.
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    ItemOpen Access
    Enhancement of misbehavior detection scheme for vehicular ad-hoc networks
    (Dhirubhai Ambani Institute of Information and Communication Technology, 2012) Jain, Shefali; Mathuria, Anish M.
    Vehicular ad hoc networks (VANETs) will facilitate various safety and non-safety applications to be deployed in the future. A vehicle in a VANET can misbehave by sending false or inaccurate information to other vehicles. Detection of such misbehavior is an important research problem. In this thesis, we study and improve an existing scheme for misbehavior detection. In that scheme, if a vehicle X generates an incorrect alert, then the nearby vehicles report the misbehavior of X to Road side unit (RSU). Upon receiving such a report, RSU imposes a fine on vehicle X. It is possible for a malicious vehicle to send a false report implicating X, even if X has generated a correct alert. As a result, the RSU may inadvertently fine an honest vehicle, potentially discouraging it from sending true alerts in the future. In this thesis, we propose a modified RSU detection algorithm to avoid honest vehicles from being fined due to malicious reports. We perform a simulation of the modified scheme and show that it identifies misbehaving vehicles with high accuracy
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    Vehicle detection and tracking
    (Dhirubhai Ambani Institute of Information and Communication Technology, 2009) Rao, K. Ramprasad; Joshi, Manjunath V.
    Real time trafficc monitoring is one of the most challenging problems in machine vision. This is one of the most sorted out research topic because of the wide spec-trum of promising applications in many areas such as smart surveillance, military applications, etc. We present a method of extracting moving targets from a real-time video stream. This approach detects and classifies vehicles in image sequences of trafficc scenes recorded by a stationary camera. Our method aims at segregating cars from non-cars and to track them through the video sequence. A classication criteria based on the features is applied to these targets to classify them into categories: cars and non-cars. Each vehicle can be described by its features. The template region is estimated by means of minimum distance approach with respect to centroid of the obtained blob of the target. Extraction of features from each frame ensures eefficiency of the tracking system.