U-RME: Underwater Refined Motion Estimation in Hazy, Cluttered and Dynamic Environments
22 Dec 2019-Vol. 1249, pp 198-208
TL;DR: A refined optical flow estimation method that performs well in case of low contrast, highly cluttered background, dynamic background, occlusion and illumination change is presented.
Abstract: Optical Flow is a popular method of computer vision for motion estimation. In this paper, we present a refined optical flow estimation method. Central to our approach is exploiting contour information as most of the motion lies on the edges. Further, we have formulated it as sparse to dense motion estimation. Proposed method has been evaluated on challenging real life image sequences of KITTI and Fish4Knowledge database. Results demonstrate that method performs well in case of low contrast, highly cluttered background, dynamic background, occlusion and illumination change.
Citations
More filters
TL;DR: DeepFish Tracking Network (DFTNet) as discussed by the authors incorporates Siamese network for encoding the appearance similarity and attention long short-term memory network to capture the motion similarity across subsequent frames, and intersection-over-union matching score is computed to amalgamate spatial similarity cue in the final score.
Abstract: Multiple fish tracking in unconstrained marine videos is a highly challenging task. Trajectories of fishes convey critical information for the analysis of fish behavior. In this article, we have proposed deep fish tracking network (DFTNet) that incorporates Siamese network for encoding the appearance similarity and attention long short-term memory network to capture the motion similarity across subsequent frames. Finally, intersection-over-union matching score is computed to amalgamate spatial similarity cue in the final score. The proposed framework can provide joint optimization score to maintain the tracklet information encoding appearance, motion, and spatial similarity cues. We perform exhaustive experiments and compare the proposed approach with competing techniques over Fish4knowledge videos and achieve significant average reduction in identification (ID) switches by 60.9%. The source code is made publicly available at https://github.com/hemanth-s17/Deep-Fish-Tracker-Network .
10 citations
18 Nov 2022
TL;DR: In this article , Simultaneous Localization and Mapping (SLAM) technology is used to provide an accurate map of the fire scene by firefighting robots, however, fire scenes are often filled with smoke and lack adequate lighting sources.
Abstract: As robotics continues to develop at an unprecedented rate, there is an increasing need to deploy robots to help humans in many fields, including firefighting areas. Firefighting robots are extremely useful in rescue work for various purposes, such as firefighting, transportation, exhaust, surveying, etc. Simultaneous localization and mapping (SLAM) technology may be used to provide an accurate map of the fire scene by firefighting robots. However, fire scenes are often filled with smoke and lack adequate lighting sources. Aiming to address some of the unfavorable conditions of fire scenes, this article reviews some solutions to similar problems in other fields and analyzes their characteristics from some previous publications on the SLAM technology for firefighting robots.
18 Nov 2022
TL;DR: In this paper , Simultaneous Localization and Mapping (SLAM) technology is used to provide an accurate map of the fire scene by firefighting robots, however, fire scenes are often filled with smoke and lack adequate lighting sources.
Abstract: As robotics continues to develop at an unprecedented rate, there is an increasing need to deploy robots to help humans in many fields, including firefighting areas. Firefighting robots are extremely useful in rescue work for various purposes, such as firefighting, transportation, exhaust, surveying, etc. Simultaneous localization and mapping (SLAM) technology may be used to provide an accurate map of the fire scene by firefighting robots. However, fire scenes are often filled with smoke and lack adequate lighting sources. Aiming to address some of the unfavorable conditions of fire scenes, this article reviews some solutions to similar problems in other fields and analyzes their characteristics from some previous publications on the SLAM technology for firefighting robots.
TL;DR: In this paper , hardware and software implementation of Motion Estimation (ME) and Haze removal techniques are reviewed and the atmospheric model for hazy images and videos is discussed. And performance metrics carried out for video analysis are also mentioned.
Abstract: Motion Estimation is used many video processing algorithms for video compression, moving object detection, 3D reconstruction, video segmentation, etc. Video standards use motion estimation in the encoder and decoder to compress the video. It becomes difficult to determine motion vectors when the video is prone to weather conditions like haze. So, there is a necessity to remove haze for video analysis. In this paper, hardware and software implementation of Motion Estimation (ME) and Haze removal techniques are reviewed. Architectures like low power and parallelism Motion Estimation techniques for Block Matching algorithms (BMA) in video coding standards and Deep Learning based methods Pixel-based Methods are studied and discussed. The atmospheric model for hazy images and videos is discussed. Techniques such as single-image dehazing are analyzed. Performance metrics carried out for video analysis are also mentioned. Based on the analysis and observation the advantages and drawbacks of these techniques are addressed.
References
More filters
TL;DR: There is a natural uncertainty principle between detection and localization performance, which are the two main goals, and with this principle a single operator shape is derived which is optimal at any scale.
Abstract: This paper describes a computational approach to edge detection. The success of the approach depends on the definition of a comprehensive set of goals for the computation of edge points. These goals must be precise enough to delimit the desired behavior of the detector while making minimal assumptions about the form of the solution. We define detection and localization criteria for a class of edges, and present mathematical forms for these criteria as functionals on the operator impulse response. A third criterion is then added to ensure that the detector has only one response to a single edge. We use the criteria in numerical optimization to derive detectors for several common image features, including step edges. On specializing the analysis to step edges, we find that there is a natural uncertainty principle between detection and localization performance, which are the two main goals. With this principle we derive a single operator shape which is optimal at any scale. The optimal detector has a simple approximate implementation in which edges are marked at maxima in gradient magnitude of a Gaussian-smoothed image. We extend this simple detector using operators of several widths to cope with different signal-to-noise ratios in the image. We present a general method, called feature synthesis, for the fine-to-coarse integration of information from operators at different scales. Finally we show that step edge detector performance improves considerably as the operator point spread function is extended along the edge.
28,073 citations
"U-RME: Underwater Refined Motion Es..." refers methods in this paper
...Edges detected by (b) Canny[5], (c) SED [7] and (d)...
[...]
...Conventional edge detection methods like Canny [5] rely on local intensity change....
[...]
...(a) Original Image (b) & (c)Enhanced Image using DehazeNet and Light Scattering Model (a) (b) (c) (d) (a) Original Image egdes detected by (b) Canny Edge Detector (c) SED (d) HED...
[...]
Proceedings Article•
24 Aug 1981TL;DR: In this paper, the spatial intensity gradient of the images is used to find a good match using a type of Newton-Raphson iteration, which can be generalized to handle rotation, scaling and shearing.
Abstract: Image registration finds a variety of applications in computer vision. Unfortunately, traditional image registration techniques tend to be costly. We present a new image registration technique that makes use of the spatial intensity gradient of the images to find a good match using a type of Newton-Raphson iteration. Our technique is taster because it examines far fewer potential matches between the images than existing techniques Furthermore, this registration technique can be generalized to handle rotation, scaling and shearing. We show how our technique can be adapted tor use in a stereo vision system.
12,944 citations
TL;DR: In this paper, a method for finding the optical flow pattern is presented which assumes that the apparent velocity of the brightness pattern varies smoothly almost everywhere in the image, and an iterative implementation is shown which successfully computes the Optical Flow for a number of synthetic image sequences.
10,727 citations
TL;DR: These comparisons are primarily empirical, and concentrate on the accuracy, reliability, and density of the velocity measurements; they show that performance can differ significantly among the techniques the authors implemented.
Abstract: While different optical flow techniques continue to appear, there has been a lack of quantitative evaluation of existing methods. For a common set of real and synthetic image sequences, we report the results of a number of regularly cited optical flow techniques, including instances of differential, matching, energy-based, and phase-based methods. Our comparisons are primarily empirical, and concentrate on the accuracy, reliability, and density of the velocity measurements; they show that performance can differ significantly among the techniques we implemented.
4,771 citations
TL;DR: The kinetic-geometric model for visual vector analysis originally developed in the study of perception of motion combinations of the mechanical type was applied to biological motion patterns and the results turned out to be highly positive.
Abstract: This paper reports the first phase of a research program on visual perception of motion patterns characteristic of living organisms in locomotion. Such motion patterns in animals and men are termed here as biological motion. They are characterized by a far higher degree of complexity than the patterns of simple mechanical motions usually studied in our laboratories. In everyday perceptions, the visual information from biological motion and from the corresponding figurative contour patterns (the shape of the body) are intermingled. A method for studying information from the motion pattern per se without interference with the form aspect was devised. In short, the motion of the living body was represented by a few bright spots describing the motions of the main joints. It is found that 10–12 such elements in adequate motion combinations in proximal stimulus evoke a compelling impression of human walking, running, dancing, etc. The kinetic-geometric model for visual vector analysis originally developed in the study of perception of motion combinations of the mechanical type was applied to these biological motion patterns. The validity of this model in the present context was experimentally tested and the results turned out to be highly positive.
4,175 citations