Jaewon Sa

Depth-Based Detection of Standing-Pigs in Moving Noise Environments

In a surveillance camera environment, the detection of standing-pigs in real-time is an important issue towards the final goal of 24-h tracking of individual pigs. In this study, we focus on depth-based detection of standing-pigs with “moving noises”, which appear every night in a commercial pig farm, but have not been reported yet. We first apply a spatiotemporal interpolation technique to remove the moving noises occurring in the depth images. Then, we detect the standing-pigs by utilizing the undefined depth values around them. Our experimental results show that this method is effective for detecting standing-pigs at night, in terms of both cost-effectiveness (using a low-cost Kinect depth sensor) and accuracy (i.e., 94.47%), even with severe moving noises occluding up to half of an input depth image. Furthermore, without any time-consuming technique, the proposed method can be executed in real-time.

Replacement Condition Detection of Railway Point Machines Using an Electric Current Sensor

Detecting replacement conditions of railway point machines is important to simultaneously satisfy the budget-limit and train-safety requirements. In this study, we consider classification of the subtle differences in the aging effect—using electric current shape analysis—for the purpose of replacement condition detection of railway point machines. After analyzing the shapes of after-replacement data and then labeling the shapes of each before-replacement data, we can derive the criteria that can handle the subtle differences between “does-not-need-to-be-replaced” and “needs-to-be-replaced” shapes. On the basis of the experimental results with in-field replacement data, we confirmed that the proposed method could detect the replacement conditions with acceptable accuracy, as well as provide visual interpretability of the criteria used for the time-series classification.

Detection of Low-Weight Pigs by Using a Top-View Camera

Caring weaning pigs is important in the management of a group-housed pig farm. In this study, we propose an automatic method for detecting low-weight pigs in a pigsty. We install a top-view camera in a room of weaning pigs to detect the motion area of each pig from the video obtained. Then, we automatically detect a low-weight pig by comparing the size of each pig. Based on the experimental results, we confirm that the proposed method can automatically detect relatively low-weight pigs without any manual inspection or measurement of actual weight by a farm administrator.

A Kinect-Based Segmentation of Touching-Pigs for Real-Time Monitoring

Segmenting touching-pigs in real-time is an important issue for surveillance cameras intended for the 24-h tracking of individual pigs. However, methods to do so have not yet been reported. We particularly focus on the segmentation of touching-pigs in a crowded pig room with low-contrast images obtained using a Kinect depth sensor. We reduce the execution time by combining object detection techniques based on a convolutional neural network (CNN) with image processing techniques instead of applying time-consuming operations, such as optimization-based segmentation. We first apply the fastest CNN-based object detection technique (i.e., You Only Look Once, YOLO) to solve the separation problem for touching-pigs. If the quality of the YOLO output is not satisfied, then we try to find the possible boundary line between the touching-pigs by analyzing the shape. Our experimental results show that this method is effective to separate touching-pigs in terms of both accuracy (i.e., 91.96%) and execution time (i.e., real-time execution), even with low-contrast images obtained using a Kinect depth sensor.

Aging Detection of Electrical Point Machines Based on Support Vector Data Description

Electrical point machines (EPM) must be replaced at an appropriate time to prevent the occurrence of operational safety or stability problems in trains resulting from aging or budget constraints. However, it is difficult to replace EPMs effectively because the aging conditions of EPMs depend on the operating environments, and thus, a guideline is typically not be suitable for replacing EPMs at the most timely moment. In this study, we propose a method of classification for the detection of an aging effect to facilitate the timely replacement of EPMs. We employ support vector data description to segregate data of “aged” and “not-yet-aged” equipment by analyzing the subtle differences in normalized electrical signals resulting from aging. Based on the before and after-replacement data that was obtained from experimental studies that were conducted on EPMs, we confirmed that the proposed method was capable of classifying machines based on exhibited aging effects with adequate accuracy.

Image Segmentation of Adjoining Pigs Using Spatio-Temporal Information

Recently, automatic video monitoring of individual pigs is emerging as an important issue in the management of group-housed pigs. Although a rich variety of studies have been reported on video monitoring techniques in intensive pig farming, it still requires further elaboration. In particular, when there exist adjoining pigs in a crowd pig room, it is necessary to have a way of separating adjoining pigs from the perspective of an image processing technique. In this paper, we propose an efficient image segmentation solution using both spatio-temporal information and region growing method for the identification of individual pigs in video surveillance systems. The experimental results with the videos obtained from a pig farm located in Sejong illustrated the efficiency of the proposed method.

Shapelets-Based Intrusion Detection for Protection Traffic Flooding Attacks

The intrusion detection for the network traffic is a technique to detect abnormal traffic flow patterns in periodic network packets. The traffic flooding attacks can be detected by the abnormal intrusion detection techniques that detects well known attack patterns. In this paper, we propose an intrusion detection way to classify normal and abnormal traffic packet pattern by converting traffic into time series data and analyzing them, and apply the information gain technique to reduce the learning execution times. That is, the normal and abnormal packet patterns are classified by applying the shapelets technique to the time-series pattern between the normal traffic and the abnormal traffic packet patterns. The experimental results show that the proposed method classifies normal patterns and traffic flooding attacks into 95% accuracy.

Segmentation of Touching Objects by using Motion Information

It is important to segment and track objects automatically in many monitoring applications. When the objects as monitored are close each other, however, it is challenging to segment each object from the touching group. Especially, if the number of touching objects is large, the segmentation accuracy degrades significantly. In this paper, we propose a method of reducing the number of touching objects which should be separated. We first detect objects by using the color information and then extract the motion information of a touching group by using GMM. By excluding the non-moving objects from the touching group, we can reduce the number of touching objects which should be separated. The experimental results show that the proposed method can exclude the non-moving objects from a touching group and thus make the segmentation problem of touching objects liable to be solved.

Efficient Workload Distribution of Photomosaic Using OpenCL into a Heterogeneous Computing Environment

ABSTRACT Recently, parallel processing methods with accelerator have been introduced into a high performance computing and a mobile computing. The photomosaic application can be parallelized by using inherent data parallelism and accelerator. In this paper, we propose a way to distribute the workload of the photomosaic application into a CPU and GPU heterogeneous computing environment. That is, the photomosaic application is parallelized using both CPU and GPU resource with the asynchronous mode of OpenCL, and then the optimal workload distribution rate is estimated by measuring the execution time with CPU-only and GPU-only distribution rates. The proposed approach is simple but very effective, and can be applied to parallelize other applications on a CPU and GPU heterogeneous computing environment. Based on the experimental results, we confirm that the performance is improved by 141% into a heterogeneous computing environment with the optimal workload distribution compared with using GPU-only method.Keywords:Heterogeneous Computing, OpenCL, Photomosaic