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Dense depth estimation is a critical application in the field of robotics and machine vision where the depth perception is essential. Unlike traditional approaches which use expensive sensors such as LiDAR (Light Detection and Ranging) devices or stereo camera setup, the proposed approach for depth estimation uses a single camera mounted on a rotating platform. This proposed setup is an effective replacement to usage of multiple cameras, which provide around view information required for some operations in the domain of autonomous vehicles and robots. Dense depth estimation of local scene is performed using the proposed setup. This is a novel, however challenging task because baseline distance between camera positions inversely affect common regions between images. The proposed work involves dense two view reconstruction and depth map merging to obtain a reliable large dense depth map.

In view of the continuous efforts by the automotive fraternity, for achieving traffic safety, detecting pedestrians from image/video has become an extensively researched topic in recent times. The task of detecting pedestrians in the urban traffic scene is complicated by the considerations involving pedestrian figure size, articulation, fast dynamics, background clutter, etc. A number of methods using different sensor technologies have been proposed in the past for the problem of pedestrian detection. To limit the scope, this paper reviews the techniques involved in day-time detection of pedestrians, with emphasis on the methods making use of a monocular visible-spectrum sensor. The paper achieves its objective by discussing the basic framework involved in detecting a pedestrian, while elaborating the requisites and the existing methodologies for implementing each stage of the basic framework.

In the research field of automotive systems, Advanced Driver Assistance Systems (ADAS) are gaining paramount importance. As the significance for such systems increase, the challenges associated with it also increases. These challenges can arise due to technology, human factors, or due to natural elements (haze, fog, rain etc.). Among these, natural challenges, especially haze, pose a major setback for technologies depending on vision sensors. It is a known fact that the presence of haze in the atmosphere degrades the driver's visibility as well as the information available with the vision based ADAS. To ensure reliability of ADAS in different climatic conditions, it is vital to get back the information of the scene degraded by haze prior to analyzing the images. In this paper, the proposed work addresses this challenge with a novel and faster image preprocessing technique that can enhances the quality of haze affected images both in terms of visibility and visual perception.

Driver safety and Advanced Driver Assistance Systems (ADAS) is gaining lot of importance these days. In some countries, there are strict regulations in place which mandate the use of certain ADAS features in automobiles. However, as the need for these safety critical systems increases, the challenges associated also increase. These challenges can arise due to technology, human factors or due to nature. In countries like India, where one can expect different weather conditions with changing geography, the associated challenges are mainly due to the natural factors like haze, fog, rain and smoke. This poses a challenging problem in terms of visibility for the drivers as well as in vision based ADAS; thereby, leading to many fatal road accidents. In this paper, a novel pre-processing technique, which addresses the interesting problem of enhancing the perceptual visibility of an image that is degraded by atmospheric haze, is proposed.