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In modern cattle farm management systems, video-based monitoring has become important in analyzing the high-level behavior of cattle for monitoring their health and predicting calving for providing timely assistance. Conventionally, sensors have been used for detecting and tracking their activities. As the body-attached sensors cause stress, video cameras can be used as an alternative. However, identifying and tracking individual cattle can be difficult, especially for black and brown varieties that are so similar in appearance. Therefore, we propose a new method of using video cameras for recognizing cattle and tracking their whereabouts. In our approach, we applied a combination of deep learning and image processing techniques to build a robust system. The proposed system processes images in separate stages, namely data pre-processing, cow detection, and cow tracking. Cow detection is performed using a popular instance segmentation network. In the cow tracking stage, for successively associating each cow with the corresponding one in the next frame, we employed the following three features: cow location, appearance features, as well as recent features of the cow region. In doing so, we simply exploited the distance between two gravity center locations of the cow regions. As color and texture suitably define the appearance of an object, we analyze the most appropriate color space to extract color moment features and use a Co-occurrence Matrix (CM) for textural representation. Deep features are extracted from recent cow images using a Convolutional Neural Network (CNN features) and are also jointly applied in the tracking process to boost system performance. We also proposed a robust Multiple Object Tracking (MOT) algorithm for cow tracking by employing multiple features from the cow region. The experimental results proved that our proposed system could handle the problems of MOT and produce reliable performance. [ABSTRACT FROM AUTHOR]

With advances in consumer electronics, demands have increased for greater granularity in differentiating and analyzing human daily activities. Moreover, the potential of machine learning, and especially deep learning, has become apparent as research proceeds in applications, such as monitoring the elderly, and surveillance for detection of suspicious people and objects left in public places. Although some techniques have been developed for human action recognition (HAR) using wearable sensors, these devices can place unnecessary mental and physical discomfort on people, especially children and the elderly. Therefore, research has focused on image-based HAR, placing it on the front line of developments in consumer electronics. This paper proposes an intelligent HAR system which can automatically recognize the human daily activities from depth sensors using human skeleton information, combining the techniques of image processing and deep learning. Moreover, due to low computational cost and high accuracy outcomes, an approach using skeleton information has proven very promising, and can be utilized without any restrictions on environments or domain structures. Therefore, this paper discusses the development of an effective skeleton information-based HAR which can be used as an embedded system. The experiments are performed using two famous public datasets of human daily activities. According to the experimental results, the proposed system outperforms other state-of-the-art methods on both datasets. [ABSTRACT FROM AUTHOR]