Classifying and learning cricket shots using camera motion

This paper presents a method to classify and learn cricket shots. The procedure begins by extracting the camera motion parameters from the shots. Then the camera parameter values are converted to symbolic form and combined to generate a symbolic description that defines the trajectory of the cricket bell. The description generated is used to classify the cricket shot and to dynamically expand or update the system's knowledge of shots. The first novel aspect of this approach is that by using the camera motion parameters, a complex and difficult process of low level image segmenting of either the batsman or the cricket ball from video images is avoided. Also the method does not require high resolution images. Another novel aspect of this work is the use of a new incremental learning algorithm that enables the system to improve and update its knowledge base. Unlike previously developed algorithms which store training instances and have simple method to prime their concept hierarchies, the incremental learning algorithm used in this work generates compact concept hierarchies and uses evidence based forgetting. The results show that the system performs well in the task of classifying four types of cricket shots.

Thermal analysis of nanofluids in microfluidics using an infrared camera

We present the thermal analysis of liquid containing Al2O3 nanoparticles in a microfluidic platform using an infrared camera. The small dimensions of the microchannel along with the low flow rates (less than 120 μl min−1) provide very low Reynolds numbers of less than 17.5, reflecting practical parameters for a microfluidic cooling platform. The heat analysis of nanofluids has never been investigated in such a regime, due to the deficiencies of conventional thermal measurement systems. The infrared camera allows non-contact, three dimensional and high resolution capability for temperature profiling. The system was studied at different w/w concentrations of thermally conductive Al2O3 nanoparticles and the experiments were in excellent agreement with the computational fluid dynamics (CFD) simulations.

Camera trapping: a contemporary approach to monitoring invasive rodents in high conservation priority ecosystems

Invasive rodent species have established on 80% of the world's islands causing significant damage to island environments. Insular ecosystems support proportionally more biodiversity than comparative mainland areas, highlighting them as critical for global biodiversity conservation. Few techniques currently exist to adequately detect, with high confidence, species that are trap-adverse such as the black rat, Rattus rattus, in high conservation priority areas where multiple non-target species persist. This study investigates the effectiveness of camera trapping for monitoring invasive rodents in high conservation areas, and the influence of habitat features and density of colonial-nesting seabirds on rodent relative activity levels to provide insights into their potential impacts. A total of 276 camera sites were established and left in situ for 8 days. Identified species were recorded in discrete 15 min intervals, referred to as 'events'. In total, 19 804 events were recorded. From these, 31 species were identified comprising 25 native species and six introduced. Two introduced rodent species were detected: the black rat (90% of sites), and house mouse Mus musculus (56% of sites). Rodent activity of both black rats and house mice were positively associated with the structural density of habitats. Density of seabird burrows was not strongly associated with relative activity levels of rodents, yet rodents were still present in these areas. Camera trapping enabled a large number of rodents to be detected with confidence in site-specific absences and high resolution to quantify relative activity levels. This method enables detection of multiple species simultaneously with low impact (for both target and non-target individuals); an ideal strategy for monitoring trap-adverse invasive rodents in high conservation areas.

Red-green-blue electrogenerated chemiluminescence utilizing a digital camera as detector

Exploiting the distinct excitation and emission properties of concomitant electrochemiluminophores in conjunction with the inherent color selectivity of a conventional digital camera, we create a new strategy for multiplexed electrogenerated chemiluminescence detection, suitable for the development of low-cost, portable clinical diagnostic devices. Red, green and blue emitters can be efficiently resolved over the three-dimensional space of ECL intensity versus applied potential and emission wavelength. As the relative contribution ratio of each emitter to the photographic RGB channels is constant, the RGB ECL intensity versus applied-potential curves could be effectively isolated to a single emitter at each potential.

Target coverage in camera networks for manufacturing workplaces

In this paper, we investigate the camera network placement problem for target coverage in manufacturing workplaces. The problem is formulated to find the minimum number of cameras of different types and their best configurations to maximise the coverage of the monitored workplace such that the given set of target points of interest are each k-covered with a predefined minimum spatial resolution. Since the problem is NP-complete, and even NP-hard to approximate, a novel method based on Simulated Annealing is presented to solve the optimisation problem. A new neighbourhood generation function is proposed to handle the discrete nature of the problem. The visual coverage is modelled using realistic and coherent assumptions of camera intrinsic and extrinsic parameters making it suitable for many real world camera based applications. Task-specific quality of coverage measure is proposed to assist selecting the best among the set of camera network placements with equal coverage. A 3D CAD of the monitored space is used to examine physical occlusions of target points. The results show the accuracy, efficiency and scalability of the presented solution method; which can be applied effectively in the design of practical camera networks.

Can camera traps monitor Komodo dragons a large ectothermic predator?

Camera trapping has greatly enhanced population monitoring of often cryptic and low abundance apex carnivores. Effectiveness of passive infrared camera trapping, and ultimately population monitoring, relies on temperature mediated differences between the animal and its ambient environment to ensure good camera detection. In ectothermic predators such as large varanid lizards, this criterion is presumed less certain. Here we evaluated the effectiveness of camera trapping to potentially monitor the population status of the Komodo dragon (Varanus komodoensis), an apex predator, using site occupancy approaches. We compared site-specific estimates of site occupancy and detection derived using camera traps and cage traps at 181 trapping locations established across six sites on four islands within Komodo National Park, Eastern Indonesia. Detection and site occupancy at each site were estimated using eight competing models that considered site-specific variation in occupancy (ψ)and varied detection probabilities (p) according to detection method, site and survey number using a single season site occupancy modelling approach. The most parsimonious model [ψ (site), p (site survey); ω = 0.74] suggested that site occupancy estimates differed among sites. Detection probability varied as an interaction between site and survey number. Our results indicate that overall camera traps produced similar estimates of detection and site occupancy to cage traps, irrespective of being paired, or unpaired, with cage traps. Whilst one site showed some evidence detection was affected by trapping method detection was too low to produce an accurate occupancy estimate. Overall, as camera trapping is logistically more feasible it may provide, with further validation, an alternative method for evaluating long-term site occupancy patterns in Komodo dragons, and potentially other large reptiles, aiding conservation of this species.

Screening for diabetic retinopathy using a non-mydriatic retinal camera in rural Victoria

PURPOSE: To develop a screening programme for the early detection of diabetic retinopathy using non-mydriatic retinal photography. METHODS: A community based screening service was offered to all people with known diabetes mellitus in selected townships in the LaTrobe and Goulburn Valleys in Victoria. At the local examination centre, basic sociodemographic information was collected as well as details of previous use of eye care services for the early detection of diabetic retinopathy. The examination included visual acuity (VA), glycosylated haemoglobin level and Polaroid photographs of each fundus using a Canon CR5-45NM non-mydriatic retinal camera (Canon, Tochigiken, Japan). Dilating drops were not used. Photographs were subsequently reviewed and letters were sent to all participants (with copies to their general practitioners) with recommendations for appropriate follow up. RESULTS: A total of 1177 people with diabetes attended the screening service, which is estimated to be 40% of the total population with known diabetes in the study area. The mean age was 65 years (range 20-94 years); 559 (48%) people reported not having a dilated fundus examination within the past 2 years; 345 (29%) people had never had a dilated fundus examination. Of the 2354 eyes, 2126 (90%) of the photographs were gradable. A total of 704 people (60%) had normal VA and no evidence of diabetic retinopathy, 209 people (18%) had diabetic retinopathy, 101 people (9%) had evidence of other fundus pathology, 42 people (3%) had reduced acuity (< 6/18) in one or both eyes (with no fundus pathology evident) and 121 people (10%) had ungradable photographs in one or both eyes. CONCLUSIONS: The present study demonstrates the usefulness of a screening programme with non-mydriatic retinal photography as an adjunct to current eye care services for the early detection of diabetic retinopathy.

Wearable cameras can reduce dietary under-reporting: doubly labelled water validation of a camera-assisted 24 h recall

Preliminary research has suggested that wearable cameras may reduce under-reporting of energy intake (EI) in self-reported dietary assessment. The aim of the present study was to test the validity of a wearable camera-assisted 24 h dietary recall against the doubly labelled water (DLW) technique. Total energy expenditure (TEE) was assessed over 15 d using the DLW protocol among forty adults (n 20 males, age 35 (sd 17) years, BMI 27 (sd 4) kg/m2 and n 20 females, age 28 (sd 7) years, BMI 22 (sd 2) kg/m2). EI was assessed using three multiple-pass 24 h dietary recalls (MP24) on days 2-4, 8-10 and 13-15. On the days before each nutrition assessment, participants wore an automated wearable camera (SenseCam (SC)) in free-living conditions. The wearable camera images were viewed by the participants following the completion of the dietary recall, and their changes in self-reported intakes were recorded (MP24+SC). TEE and EI assessed by the MP24 and MP24+SC methods were compared. Among men, the MP24 and MP24+SC measures underestimated TEE by 17 and 9%, respectively (P< 0.001 and P= 0.02). Among women, these measures underestimated TEE by 13 and 7%, respectively (P< 0.001 and P= 0.004). The assistance of the wearable camera (MP24+SC) reduced the magnitude of under-reporting by 8% for men and 6% for women compared with the MP24 alone (P< 0.001 and P< 0.001). The increase in EI was predominantly from the addition of 265 unreported foods (often snacks) as revealed by the participants during the image review. Wearable cameras enhance the accuracy of self-report by providing passive and objective information regarding dietary intake. High-definition image sensors and increased imaging frequency may improve the accuracy further.

Camera model identification with unknown models

Feature based camera model identification plays an important role for forensics investigations on images. The conventional feature based identification schemes suffer from the problem of unknown models, that is, some images are captured by the camera models previously unknown to the identification system. To address this problem, we propose a new scheme: Source Camera Identification with Unknown models (SCIU). It has the capability of identifying images of the unknown models as well as distinguishing images of the known models. The new SCIU scheme consists of three stages: 1) unknown detection; 2) unknown expansion; and 3) (K+1)-class classification. Unknown detection applies a k-nearest neighbours method to recognize a few sample images of unknown models from the unlabeled images. Unknown expansion further extends the set of unknown sample images using a self-training strategy. Then, we address a specific (K+1)-class classification, in which the sample images of unknown (1-class) and known models (K-class) are combined to train a classifier. In addition, we develop a parameter optimization method for unknown detection, and investigate the stopping criterion for unknown expansion. The experiments carried out on the Dresden image collection confirm the effectiveness of the proposed SCIU scheme. When unknown models present, the identification accuracy of SCIU is significantly better than the four state-of-art methods: 1) multi-class Support Vector Machine (SVM); 2) binary SVM; 3) combined classification framework; and 4) decision boundary carving.