Reconstruction of the pose of uncalibrated cameras via user-generated videos

Extraction of 3D geometry from hand-held unsteady uncalibrated cameras faces multiple difficulties: finding usable frames, feature-matching and unknown variable focal length to name three. We have built a prototype system to allow a user to spatially navigate playback viewpoints of an event of interest, using geometry automatically recovered from casually captured videos. The system, whose workings we present in this paper, necessarily estimates not only scene geometry, but also relative viewpoint position, overcoming the mentioned difficulties in the process. The only inputs required are video sequences from various viewpoints of a common scene, as are readily available online from sporting and music events. Our methods make no assumption of the synchronization of the input and do not require file metadata, instead exploiting the video to self-calibrate. The footage need only contain some camera rotation with little translation—for hand-held event footage a likely occurrence.This is the author accepted manuscript. The final version is available from IEEE via http://dx.doi.org/10.1145/2659021.265902

Real-time event detection in field sport videos

This chapter describes a real-time system for event detection in sports broadcasts. The approach presented is applicable to a wide range of field sports. Using two independent event detection approaches that work simultaneously, the system is capable of accurately detecting scores, near misses, and other exciting parts of a game that do not result in a score. The results obtained across a diverse dataset of different field sports are promising, demonstrating over 90% accuracy for a feature-based event detector and 100% accuracy for a scoreboard-based detector detecting only score

MULTI-SCALE SEMI-TRANSPARENT BLOTCH REMOVAL ON ARCHIVED PHOTOGRAPHS USING BAYESIAN MATTING TECHNIQUES AND VISIBILITY LAWS

ABSTRACT This paper presents an automatic technique to remove semitransparent blotches (due to moisture) from archived photographs and documents. Blotches are processed in the HSV space. While chroma components are processed using a simple texture synthesis method, the intensity component is split into an over-complete wavelet representation. In the approximation band, the blotch is modelled as an alpha matte which reduces the intensity of the image in a non-uniform yet smooth manner. The alpha matte is estimated using a Bayesian approach and its effect reversed. Wavelet details are left unchanged in the case of perfect semi-transparency or attenuated using visibility laws whenever dirt and dust cause spurious edges. Experimental results achieved on many historical photographs show the effectiveness of the proposed approach

A Low-Complexity Mosaicing Algorithm for Stock Assessment of Seabed-Burrowing Species

Peer-reviewed This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination. Manuscript received January 27, 2017; revised August 17, 2017 and December 27, 2017; accepted February 16, 2018. Published in: IEEE Journal of Oceanic Engineering ( Early Access ) DOI: 10.1109/JOE.2018.2808973This paper proposes an algorithm for mosaicing videos generated during stock assessment of seabed-burrowing species. In these surveys, video transects of the seabed are captured and the population is estimated by counting the number of burrows in the video. The mosaicing algorithm is designed to process a large amount of video data and summarize the relevant features for the survey in a single image. Hence, the algorithm is designed to be computationally inexpensive while maintaining a high degree of robustness. We adopt a registration algorithm that employs a simple translational motion model and generates a mapping to the mosaic coordinate system using a concatenation of frame-by-frame homographies. A temporal smoothness prior is used in a maximum a posteriori homography estimation algorithm to reduce noise in the motion parameters in images with small amounts of texture detail. A multiband blending scheme renders the mosaic and is optimized for the application requirements. Tests on a large data set show that the algorithm is robust enough to allow the use of mosaics as a medium for burrow counting. This will increase the verifiability of the stock assessments as well as generate a ground truth data set for the learning of an automated burrow counting algorithm.This work was supported by the Science Foundation Ireland under Award SFI-PI 08/IN.1/I2112

Mosaics For Burrow Detection in Underwater Surveillance Video

Harvesting the commercially significant lobster,Nephrops norvegicus, is a multimillion dollar industry in Europe. Stock assessment is essential for maintaining this activity but it is conducted by manually inspecting hours of underwater surveillance videos. To improve this tedious process, we propose the use of mosaics for the automated detection of burrows on the seabed. We present novel approaches for handling the difficult lighting conditions that cause poor video quality in this kind of video material. Mosaics are built using 1-10 minutes of footage and candidate burrows are selected using image segmentation based on local image contrast. A K-Nearest Neighbour classifier is then used to select burrows from these candidate regions. Our final decision accuracy at 93.6% recall and 86.6% precision shows a corresponding 18% and 14.2% improvement compared with previous work.Funder: Science Foundation Ireland PI Programme: SFI-PI 08/IN.1/I211

Mosaics For Nephrops Detection in Underwater Survey Videos

Harvesting the commercially significant lobster, Nephrops norvegicus, is a multimillion dollar industry in Europe. Stock assessment is essential for maintaining this activity but it is conducted by manually inspecting hours of underwater surveillance videos. To improve this tedious process, we propose an automated procedure. This procedure uses mosaics for detecting the Nephrops, which improves visibility and reduces the tedious video inspection process to the browsing of a single image. In addition to this novel application approach, key contributions are made for handling the difficult lighting conditions in these kinds of videos. Mosaics are build using 1-10 minutes of footage and candidate Nephrops regions are selected using image segmentation based on local image contrast and colour features. A K-Nearest Neighbour classifier is then used to select the respective Nephrops from these candidate regions. Our final decision accuracy at 87.5% recall and precision shows a corresponding 31.5% and 79.4% improvement compared with previous work