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Optical Flow Estimation is an essential component for many image processing techniques. This field of research in computer vision has seen an amazing development in recent years. In particular, the introduction of Convolutional Neural Networks for optical flow estimation has shifted the paradigm of research from the classical traditional approach to deep learning side. At present, state of the art techniques for optical flow are based on convolutional neural networks and almost all top performing methods incorporate deep learning architectures in their schemes. This paper presents a brief analysis of optical flow estimation techniques and highlights most recent developments in this field. A comparison of the majority of pertinent traditional and deep learning methodologies has been undertaken resulting the detailed establishment of the respective advantages and disadvantages of the traditional and deep learning categories. An insight is provided into the significant factors that affect the success or failure of the two classes of optical flow estimation. In establishing the foremost existing and inherent challenges with traditional and deep learning schemes, probable solutions have been proposed indeed.

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Dense depth estimation based on a single image is a basic problem in computer vision and has exciting applications in many robotic tasks. Modelling fully supervised methods requires the acquisition of accurate and large ground truth data sets, which is often complex and expensive. On the other hand, self-supervised learning has emerged as a promising alternative to monocular depth estimation as it does not require ground truth depth data. In this paper, we propose a novel self-supervised joint learning framework for depth estimation using consecutive frames from monocular and stereo videos. Our architecture leverages two new ideas for improvement: (1) triplet attention and (2) funnel activation (FReLU). By adding triplet attention to the deep and pose networks, this module captures the importance of features across dimensions in a tensor without any information bottlenecks, making the optimisation learning framework more reliable. FReLU is used at the non-linear activation layer to grasp the local context adaptively in images, rather than using more complex convolutions at the convolution layer. FReLU extracts the spatial structure of objects by the pixel-wise modeling