Automatic detection, tracking and counting of birds in marine video content

Robust automatic detection of moving objects in a marine context is a multi-faceted problem due to the complexity of the observed scene. The dynamic nature of the sea caused by waves, boat wakes, and weather conditions poses huge challenges for the development of a stable background model. Moreover, camera motion, reflections, lightning and illumination changes may contribute to false detections. Dynamic background subtraction (DBGS) is widely considered as a solution to tackle this issue in the scope of vessel detection for maritime traffic analysis. In this paper, the DBGS techniques suggested for ships are investigated and optimized for the monitoring and tracking of birds in marine video content. In addition to background subtraction, foreground candidates are filtered by a classifier based on their feature descriptors in order to remove non-bird objects. Different types of classifiers have been evaluated and results on a ground truth labeled dataset of challenging video fragments show similar levels of precision and recall of about 95% for the best performing classifier. The remaining foreground items are counted and birds are tracked along the video sequence using spatio-temporal motion prediction. This allows marine scientists to study the presence and behavior of birds

HOG, LBP and SVM based Traffic Density Estimation at Intersection

Increased amount of vehicular traffic on roads is a significant issue. High amount of vehicular traffic creates traffic congestion, unwanted delays, pollution, money loss, health issues, accidents, emergency vehicle passage and traffic violations that ends up in the decline in productivity. In peak hours, the issues become even worse. Traditional traffic management and control systems fail to tackle this problem. Currently, the traffic lights at intersections aren't adaptive and have fixed time delays. There's a necessity of an optimized and sensible control system which would enhance the efficiency of traffic flow. Smart traffic systems perform estimation of traffic density and create the traffic lights modification consistent with the quantity of traffic. We tend to propose an efficient way to estimate the traffic density on intersection using image processing and machine learning techniques in real time. The proposed methodology takes pictures of traffic at junction to estimate the traffic density. We use Histogram of Oriented Gradients (HOG), Local Binary Patterns (LBP) and Support Vector Machine (SVM) based approach for traffic density estimation. The strategy is computationally inexpensive and can run efficiently on raspberry pi board. Code is released at https://github.com/DevashishPrasad/Smart-Traffic-Junction.Comment: paper accepted at IEEE PuneCon 201

A Petal of the Sunflower: Photometry of the Stellar Tidal Stream in the Halo of Messier 63 (NGC 5055)

We present surface photometry of a very faint, giant arc feature in the halo of the nearby spiral galaxy NGC 5055 (M63) that is consistent with being a part of a stellar stream resulting from the disruption of a dwarf satellite galaxy. This faint feature was first detected in early photographic studies by van der Kruit (1979); more recently by Mart\'inez-Delgado et al. (2010) and as presented in this work, the loop has been realized to be the result of a recent minor merger through evidence obtained by deep images taken with a telescope of only 0.16 m aperture. The stellar stream is confirmed in additional images taken with the 0.5 m of the BlackBird Remote Observatory and the 0.8 m of the McDonald Observatory. This low surface brightness structure around the disk of the galaxy extends ~29 kpc from its center, with a projected width of 3.3 kpc. The stream's morphology is consistent with that of the visible part of a "great-circle" stellar stream originating from the accretion of a ~10^8 M_sun dwarf satellite in the last few Gyr. The progenitor satellite's current position and fate are not conclusive from our data. The color of the stream's stars is consistent with Local Group dwarfs and is similar to the outer regions of M63's disk and stellar halo. We detect other low surface brightness "plumes"; some of these may be extended spiral features related to the galaxy's complex spiral structure and others may be tidal debris associated with the disruption of the galaxy's outer stellar disk as a result of the accretion event. We differentiate between features related to the tidal stream and faint, blue features in the outskirts of the galaxy's disk previously detected by the GALEX satellite. With its highly warped HI gaseous disk (~20 deg), M63 represents one of several examples of an isolated spiral galaxy with a warped disk showing strong evidence of an ongoing minor merger.Comment: 16 pages, 10 figures, 3 tables, Accepted for publication in The Astronomical Journa

Detecting shadows and low-lying objects in indoor and outdoor scenes using homographies

Many computer vision applications apply background suppression techniques for the detection and segmentation of moving objects in a scene. While these algorithms tend to work well in controlled conditions they often fail when applied to unconstrained real-world environments. This paper describes a system that detects and removes erroneously segmented foreground regions that are close to a ground plane. These regions include shadows, changing background objects and other low-lying objects such as leaves and rubbish. The system uses a set-up of two or more cameras and requires no 3D reconstruction or depth analysis of the regions. Therefore, a strong camera calibration of the set-up is not necessary. A geometric constraint called a homography is exploited to determine if foreground points are on or above the ground plane. The system takes advantage of the fact that regions in images off the homography plane will not correspond after a homography transformation. Experimental results using real world scenes from a pedestrian tracking application illustrate the effectiveness of the proposed approach

Feature-based image patch classification for moving shadow detection

Moving object detection is a first step towards many computer vision applications, such as human interaction and tracking, video surveillance, and traffic monitoring systems. Accurate estimation of the target object’s size and shape is often required before higher-level tasks (e.g., object tracking or recog nition) can be performed. However, these properties can be derived only when the foreground object is detected precisely. Background subtraction is a common technique to extract foreground objects from image sequences. The purpose of background subtraction is to detect changes in pixel values within a given frame. The main problem with background subtraction and other related object detection techniques is that cast shadows tend to be misclassified as either parts of the foreground objects (if objects and their cast shadows are bonded together) or independent foreground objects (if objects and shadows are separated). The reason for this phenomenon is the presence of similar characteristics between the target object and its cast shadow, i.e., shadows have similar motion, attitude, and intensity changes as the moving objects that cast them. Detecting shadows of moving objects is challenging because of problem atic situations related to shadows, for example, chromatic shadows, shadow color blending, foreground-background camouflage, nontextured surfaces and dark surfaces. Various methods for shadow detection have been proposed in the liter ature to address these problems. Many of these methods use general-purpose image feature descriptors to detect shadows. These feature descriptors may be effective in distinguishing shadow points from the foreground object in a specific problematic situation; however, such methods often fail to distinguish shadow points from the foreground object in other situations. In addition, many of these moving shadow detection methods require prior knowledge of the scene condi tions and/or impose strong assumptions, which make them excessively restrictive in practice. The aim of this research is to develop an efficient method capable of addressing possible environmental problems associated with shadow detection while simultaneously improving the overall accuracy and detection stability. In this research study, possible problematic situations for dynamic shad ows are addressed and discussed in detail. On the basis of the analysis, a ro bust method, including change detection and shadow detection, is proposed to address these environmental problems. A new set of two local feature descrip tors, namely, binary patterns of local color constancy (BPLCC) and light-based gradient orientation (LGO), is introduced to address the identified problematic situations by incorporating intensity, color, texture, and gradient information. The feature vectors are concatenated in a column-by-column manner to con struct one dictionary for the objects and another dictionary for the shadows. A new sparse representation framework is then applied to find the nearest neighbor of the test image segment by computing a weighted linear combination of the reference dictionary. Image segment classification is then performed based on the similarity between the test image and the sparse representations of the two classes. The performance of the proposed framework on common shadow detec tion datasets is evaluated, and the method shows improved performance com pared with state-of-the-art methods in terms of the shadow detection rate, dis crimination rate, accuracy, and stability. By achieving these significant improve ments, the proposed method demonstrates its ability to handle various problems associated with image processing and accomplishes the aim of this thesis

Convolutional neural network architecture for geometric matching

We address the problem of determining correspondences between two images in agreement with a geometric model such as an affine or thin-plate spline transformation, and estimating its parameters. The contributions of this work are three-fold. First, we propose a convolutional neural network architecture for geometric matching. The architecture is based on three main components that mimic the standard steps of feature extraction, matching and simultaneous inlier detection and model parameter estimation, while being trainable end-to-end. Second, we demonstrate that the network parameters can be trained from synthetically generated imagery without the need for manual annotation and that our matching layer significantly increases generalization capabilities to never seen before images. Finally, we show that the same model can perform both instance-level and category-level matching giving state-of-the-art results on the challenging Proposal Flow dataset.Comment: In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017

Optical Colors of Intracluster Light in the Virgo Cluster Core

We continue our deep optical imaging survey of the Virgo cluster using the CWRU Burrell Schmidt telescope by presenting B-band surface photometry of the core of the Virgo cluster in order to study the cluster's intracluster light (ICL). We find ICL features down to mu_b ~ 29 mag sq. arcsec, confirming the results of Mihos et al. (2005), who saw a vast web of low-surface brightness streams, arcs, plumes, and diffuse light in the Virgo cluster core using V-band imaging. By combining these two data sets, we are able to measure the optical colors of many of the cluster's low-surface brightness features. While much of our imaging area is contaminated by galactic cirrus, the cluster core near the cD galaxy, M87, is unobscured. We trace the color profile of M87 out to over 2000 arcsec, and find a blueing trend with radius, continuing out to the largest radii. Moreover, we have measured the colors of several ICL features which extend beyond M87's outermost reaches and find that they have similar colors to the M87's halo itself, B-V ~ 0.8. The common colors of these features suggests that the extended outer envelopes of cD galaxies, such as M87, may be formed from similar streams, created by tidal interactions within the cluster, that have since dissolved into a smooth background in the cluster potential.Comment: 14 pages. Published in ApJ, September 201

Multi-Wavelength View of Kiloparsec-Scale Clumps in Star-Forming Galaxies at z~2

This paper studies the properties of kiloparsec-scale clumps in star-forming galaxies at z~2 through multi-wavelength broad band photometry. A sample of 40 clumps is identified through auto-detection and visual inspection from 10 galaxies with 1.5<z<2.5 in the Hubble Ultra Deep Field, where deep and high-resolution HST/WFC3 and ACS images enable us to resolve structures of z~2 galaxies down to kpc scale in the rest-frame UV and optical bands as well as to detect clumps toward the faint end. The physical properties of clumps are measured through fitting spatially resolved seven-band (BVizYJH) spectral energy distribution to models. On average, the clumps are blue and have similar median rest-frame UV--optical color as the diffuse components of their host galaxies, but the clumps have large scatter in their colors. Although the star formation rate (SFR)--stellar mass relation of galaxies is dominated by the diffuse components, clumps emerge as regions with enhanced specific SFRs, contributing individually ~10% and together ~50% of the SFR of the host galaxies. However, the contributions of clumps to the rest-frame UV/optical luminosity and stellar mass are smaller, typically a few percent individually and ~20% together. On average, clumps are younger by 0.2 dex and denser by a factor of 8 than diffuse components. Clump properties have obvious radial variations in the sense that central clumps are redder, older, more extincted, denser, and less active on forming stars than outskirts clumps. Our results are broadly consistent with a widely held view that clumps are formed through gravitational instability in gas-rich turbulent disks and would eventually migrate toward galactic centers and coalesce into bulges. Roughly 40% of the galaxies in our sample contain a massive clump that could be identified as a proto-bulge, which seems qualitatively consistent with such a bulge-formation scenario.Comment: Accepted by ApJ. This updated version matches the in-press one. 50 pages (single column), 10 figures, 3 table