Deep convolutional neural networks (DCNNs) offer an effective hierarchical representation of images for various vision analysis tasks, including classification and detection. In this paper, we propose to study background modeling and object segmentation from highly cluttered natural scenes in the DCNN feature domain instead of traditional pixel domain. Specifically, we first design and train a DCNN for animal-human-background object classification, which is used to analyze the input image to generate multi-layer feature maps, representing the responses of different image regions to the animal-human-background classifier. From these feature maps, we construct the so-called deep objectness graph for accurate animal-human object segmentation with graph cut. The segmented object regions from each image in the sequence are then verified and fused in the temporal domain using background modeling. Recognizing that the DCNN is very computation-intensive, we explore a fast and efficient design of the DCNN which finds a good trade-off between complexity and the classification-segmentation performance. Our experimental results demonstrate that our proposed method outperforms existing state-of-the-art methods on the camera-trap dataset with highly cluttered natural scenes.

In this paper, we couple effective dynamic background modeling with deep learning classification to develop a fast and accurate scheme for human-animal detection from highly cluttered camera-trap images using joint background modeling and deep learning classification. Specifically, first, we develop an effective background modeling and subtraction scheme to generate region proposals for the foreground objects. We then develop a cross-frame image patch verification to reduce the number of foreground object proposals. Finally, we perform complexity-accuracy analysis of deep convolutional neural networks (DCNN) to develop a fast deep learning classification scheme to classify these region proposals into three categories: human, animals, and background patches. The optimized DCNN is able to maintain high level of accuracy while reducing the computational complexity by 14 times. Our experimental results demonstrate that the proposed method outperforms existing methods on the camera-trap dataset.

We proposed a novel deep convolutional neural network based species recognition algorithm for wild animal classification on very challenging camera-trap imagery data. The imagery data were captured with motion triggered camera trap and were segmented automatically using the state of the art graph-cut algorithm. The moving foreground is selected as the region of interests and is fed to the proposed species recognition algorithm. For the comparison purpose, we use the traditional bag of visual words model as the baseline species recognition algorithm. It is clear that the proposed deep convolutional neural network based species recognition achieves superior performance. To our best knowledge, this is the first attempt to the fully automatic computer vision based species recognition on the real camera-trap images. We also collected and annotated a standard camera-trap dataset of 20 species common in North America, which contains 14, 346 training images and 9, 530 testing images, and is available to public for evaluation and benchmark purpose.

With the development of positioning technology, movement data has become widely available nowadays. An important task in movement data analysis is to mine the relationships among moving objects based on their spatiotemporal interactions. Among all relationship types, attraction and avoidance are arguably the most natural ones. However, rather surprisingly, there is no existing method that addresses the problem of mining significant attraction and avoidance relationships in a well-defined and unified framework. In this paper, we propose a novel method to measure the significance value of relationship between any two objects by examining the background model of their movements via permutation test. Since permutation test is computationally expensive, two effective pruning strategies are developed to reduce the computation time. Furthermore, we show how the proposed method can be extended to efficiently answer the classic threshold query: given an object, retrieve all the objects in the database that have relationships, whose significance values are above certain threshold, with the query object. Empirical studies on both synthetic data and real movement data demonstrate the effectiveness and efficiency of our method.

How do animals use their habitat? Where do they go and what do they do? These basic questions are key not only to understanding a species’ ecology and evolution, but also for addressing many of the environmental challenges we currently face, including problems posed by invasive species, the spread of zoonotic diseases and declines in wildlife populations due to anthropogenic climate and land-use changes. Monitoring the movements and activities of wild animals can be difficult, especially when the species in question are small, cryptic or move over large areas. In this paper, we describe an Automated Radio-Telemetry System (ARTS) that we designed and built on Barro Colorado Island (BCI), Panama to overcome these challenges. We describe the hardware and software we used to implement the ARTS, and discuss the scientific successes we have had using the system, as well as the logistical challenges we faced in maintaining the system in real-world, rainforest conditions. The ARTS uses automated radio-telemetry receivers mounted on 40-m towers topped with arrays of directional antennas to track the activity and location of radio-collared study animals, 24 h a day, 7 days a week. These receiving units are connected by a wireless network to a server housed in the laboratory on BCI, making these data available in real time to researchers via a web-accessible database. As long as study animals are within the range of the towers, the ARTS system collects data more frequently than typical animal-borne global positioning system collars (∼12 locations/h) with lower accuracy (approximately 50 m) but at much reduced cost per tag (∼10X less expensive). The geographic range of ARTS, like all VHF telemetry, is affected by the size of the radio-tag as well as its position in the forest (e.g. tags in the canopy transmit farther than those on the forest floor). We present a model of signal propagation based on landscape conditions, which quantifies these effects and identifies sources of interference, including weather events and human activity. ARTS has been used to track 374 individual animals from 38 species, including 17 mammal species, 12 birds, 7 reptiles or amphibians, as well as two species of plant seeds. These data elucidate the spatio-temporal dynamics of animal activity and movement at the site and have produced numerous peer-reviewed publications, student theses, magazine articles, educational programs and film documentaries. These data are also relevant to long-term population monitoring and conservation plans. Both the successes and the failures of the ARTS system are applicable to broader sensor network applications and are valuable for advancing sensor network research.

With the maturity and wide availability of GPS, wireless, telecommunication, and Web technologies, massive amounts of object movement data have been collected from various moving object targets, such as animals, mobile devices, vehicles, and climate radars. Analyzing such data has deep implications in many applications, such as, ecological study, traffic control, mobile communication management, and climatological forecast. In this article, we focus our study on animal movement data analysis and examine advanced data mining methods for discovery of various animal movement patterns. In particular, we introduce a moving object data mining system, MoveMine, which integrates multiple data mining functions, including sophisticated pattern mining and trajectory analysis. In this system, two interesting moving object pattern mining functions are newly developed: (1) periodic behavior mining and (2) swarm pattern mining . For mining periodic behaviors, a reference location-based method is developed, which first detects the reference locations, discovers the periods in complex movements, and then finds periodic patterns by hierarchical clustering. For mining swarm patterns, an efficient method is developed to uncover flexible moving object clusters by relaxing the popularly-enforced collective movement constraints. In the MoveMine system, a set of commonly used moving object mining functions are built and a user-friendly interface is provided to facilitate interactive exploration of moving object data mining and flexible tuning of the mining constraints and parameters. MoveMine has been tested on multiple kinds of real datasets, especially for MoveBank applications and other moving object data analysis. The system will benefit scientists and other users to carry out versatile analysis tasks to analyze object movement regularities and anomalies. Moreover, it will benefit researchers to realize the importance and limitations of current techniques and promote future studies on moving object data mining. As expected, a mastery of animal movement patterns and trends will improve our understanding of the interactions between and the changes of the animal world and the ecosystem and therefore help ensure the sustainability of our ecosystem.

Periodicity is a frequently happening phenomenon for moving objects. Finding periodic behaviors is essential to understanding object movements. However, periodic behaviors could be complicated, involving multiple interleaving periods, partial time span, and spatiotemporal noises and outliers.

With the maturity of GPS, wireless, and Web technologies, increasing amounts of movement data collected from various moving objects, such as animals, vehicles, mobile devices, and climate radars, have become widely available. Analyzing such data has broad applications, e.g., in ecological study, vehicle control, mobile communication management, and climatological forecast. However, few data mining tools are available for flexible and scalable analysis of massive-scale moving object data. Our system, MoveMine, is designed for sophisticated moving object data mining by integrating several attractive functions including moving object pattern mining and trajectory mining. We explore the state-of-the-art and novel techniques at implementation of the selected functions. A user-friendly interface is provided to facilitate interactive exploration of mining results and flexible tuning of the underlying methods. Since MoveMine is tested on multiple kinds of real data sets, it will benefit users to carry out versatile analysis on these kinds of data. At the same time, it will benefit researchers to realize the importance and limitations of current techniques as well as the potential future studies in moving object data mining.

Recent improvements in positioning technology make massive moving object data widely available. One important analysis is to find the moving objects that travel together. Existing methods put a strong constraint in defining moving object cluster, that they require the moving objects to stick together for consecutive timestamps. Our key observation is that the moving objects in a cluster may actually diverge temporarily and congregate at certain timestamps. Motivated by this, we propose the concept of swarm which captures the moving objects that move within arbitrary shape of clusters for certain timestamps that are possibly non-consecutive. The goal of our paper is to find all discriminative swarms, namely closed swarm . While the search space for closed swarms is prohibitively huge, we design a method, ObjectGrowth, to efficiently retrieve the answer. In ObjectGrowth, two effective pruning strategies are proposed to greatly reduce the search space and a novel closure checking rule is developed to report closed swarms on-the-fly. Empirical studies on the real data as well as large synthetic data demonstrate the effectiveness and efficiency of our methods.

Studying animal movement and distribution is of critical importance to addressing environmental challenges including invasive species, infectious diseases, climate and land-use change. Motion sensitive camera traps offer a visual sensor to record the presence of a species at a location, recording their movement in the Eulerian sense. Modern digital camera traps that record video present new analytical opportunities, but also new data management challenges. This paper describes our experience with a year-long terrestrial animal monitoring system at Barro Colorado Island, Panama. The data gathered from our camera network shows the spatio-temporal dynamics of terrestrial bird and mammal activity at the site-data relevant to immediate science questions, and long-term conservation issues. We believe that the experience gained and lessons learned during our year long deployment and testing of the camera traps are applicable to broader sensor network applications and are valuable for the advancement of the sensor network research. We suggest that the continued development of these hardware, software, and analytical tools, in concert, offer an exciting sensor-network solution to monitoring of animal populations which could realistically scale over larger areas and time spans.