Big data applications are consuming most of the space in industry and research area. Among the widespread examples of big data, the role of video streams from CCTV cameras is equally important as other sources like social media data, sensor data, agriculture data, medical data and data evolved from space research. Surveillance videos have a major contribution in unstructured big data. CCTV cameras are implemented in all places where security having much importance. Manual surveillance seems tedious and time consuming. Security can be defined in different terms in different contexts like theft identification, violence detection, chances of explosion etc. In crowded public places the term security covers almost all type of abnormal events. Among them violence detection is difficult to handle since it involves group activity. The anomalous or abnormal activity analysis in a crowd video scene is very difficult due to several real world constraints. The paper includes a deep rooted survey which starts from object recognition, action recognition, crowd analysis and finally violence detection in a crowd environment. Majority of the papers reviewed in this survey are based on deep learning technique. Various deep learning methods are compared in terms of their algorithms and models. The main focus of this survey is application of deep learning techniques in detecting the exact count, involved persons and the happened activity in a large crowd at all climate conditions. Paper discusses the underlying deep learning implementation technology involved in various crowd video analysis methods. Real time processing, an important issue which is yet to be explored more in this field is also considered. Not many methods are there in handling all these issues simultaneously. The issues recognized in existing methods are identified and summarized. Also future direction is given to reduce the obstacles identified. The survey provides a bibliographic summary of papers from ScienceDirect, IEEE Xplore and ACM digital library.

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Intelligent video surveillance: a review through deep learning techniques for crowd analysis

Smart wearables collect and analyze data, and in some scenarios make a smart decision and provide a response to the user and are finding more and more applications in our daily life. In this paper, we comprehensively survey the most recent and important research works conducted in the area of wearable Internet of Things (IoT) and classify the wearables into four major clusters: (i) health, (ii) sports and daily activity, (iii) tracking and localization, and (iv) safety. The fundamental differences of the algorithms associated within each cluster are grouped and analyzed and the research challenges and open issues in each cluster are discussed. This survey reveals that although Cellular IoT (CIoT) has many advantages and can bring enormous applications to IoT wearables, it has been rarely studied by the researchers. This article also addresses the opportunities and challenges related to implementing CIoT-enabled wearables.

Wearables and the Internet of Things (IoT), Applications, Opportunities, and Challenges: A Survey

A review of state-of-the-art techniques for abnormal human activity recognition

Falls are abnormal activity events that occur infrequently; however, they are serious health problems among elderly individuals. With the advancements of technologies, falls have been widely studied by scientific researchers to minimize serious consequences and negative impacts. Fall detection and fall prevention are two strategies to tackle fall issues with a variety of sensing techniques and classifier models. Currently, many reviews on fall-related technologies have been presented and analyzed; however, most of them give surveys on the subfield of fall-related systems, while others are not extensive and comprehensive reviews. In fact, the latest researches have a new trend of fusion-based methods to improve the performance of the fall-related systems based on a combination of different sensors or classifier models. Adaptive threshold and radio frequency-based systems are also researched and proposed recently, which are seldom mentioned in other reviews. Therefore, a global taxonomy for current fall-related studies from four aspects, including current literature reviews, fall detection, and prevention systems based on different sensor apparatus and analytic algorithm, low power techniques, and sensor placements for fall-related systems are conducted in this paper. Several research challenges and issues in the fall-related field are also discussed and analyzed. The objective of this review paper is to conclude and provide a good position of current fall-related studies to inspire researchers in this field.

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Research of Fall Detection and Fall Prevention Technologies: A Systematic Review

Falling is among the most damaging event elderly people may experience. With the ever-growing aging population, there is an urgent need for the development of fall detection systems. Thanks to the rapid development of sensor networks and the Internet of Things (IoT), human-computer interaction using sensor fusion has been regarded as an effective method to address the problem of fall detection. In this paper, we provide a literature survey of work conducted on elderly fall detection using sensor networks and IoT. Although there are various existing studies which focus on the fall detection with individual sensors, such as wearable ones and depth cameras, the performance of these systems are still not satisfying as they suffer mostly from high false alarms. Literature shows that fusing the signals of different sensors could result in higher accuracy and lower false alarms, while improving the robustness of such systems. We approach this survey from different perspectives, including data collection, data transmission, sensor fusion, data analysis, security, and privacy. We also review the benchmark data sets available that have been used to quantify the performance of the proposed methods. The survey is meant to provide researchers in the field of elderly fall detection using sensor networks with a summary of progress achieved up to date and to identify areas where further effort would be beneficial.

/pdf/elderly-fall-detection-systems-a-literature-survey-9nx87469di.pdf

Elderly Fall Detection Systems: A Literature Survey

Automatic human fall detection is one important research topic in caring for vulnerable people, such as elders at home and patients in medical places. Over the past decade, numerous methods aiming at solving the problem were proposed. However, the existing methods only focus on detecting human themselves and cannot work effectively in complicated environments, especially for the falls on furniture. To alleviate this problem, a new method for human fall detection on furniture using scene analysis based on deep learning and activity characteristics is presented in this paper. The proposed method first performs scene analysis using a deep learning method faster R-CNN to detect human and furniture. Meanwhile, the space relation between human and furniture is detected. The activity characteristics of the detected people, such as human shape aspect ratio, centroid, motion speed are detected and tracked. Through measuring the changes of these characteristics and judging the relations between the people and furniture nearby, the falls on furniture can be effectively detected. Experiment results demonstrated that our approach not only accurately and effectively detected falls on furniture, such as sofa and chairs but also distinguished them from other fall-like activities, such as sitting or lying down, while the existing methods have difficulties to handle these. In our experiments, our algorithm achieved 94.44% precision, 94.95% recall, and 95.50% accuracy. The proposed method can be potentially used and integrated as a medical assistance in health care and medical places and appliances.

Detection of Human Falls on Furniture Using Scene Analysis Based on Deep Learning and Activity Characteristics

Falls sustained by subjects can have severe consequences, especially for elderly persons living alone. A fall detection method for indoor environments based on the Kinect sensor and analysis of three-dimensional skeleton joints information is proposed. Compared with state-of-the-art methods, the authors' method provides two major improvements. First, possible fall activity is quantified and represented by a one-dimensional float array with only 32 items, followed by fall recognition using a support vector machine (SVM). Unlike typical deep learning methods, the input parameters of their method are dramatically reduced. Hence, videos are trained and recognised by an SVM with a low time cost. Second, the torso angle is imported to detect the start key frame of a possible fall, which is much more efficient than using a sliding window. Their approach is evaluated on the telecommunication systems team (TST) fall detection dataset v2. The results show that their approach achieves an accuracy of 92.05%, better than other typical methods. According to the characters of machine learning, when more samples are imported, their method is expected to achieve a higher accuracy and stronger capability of fall-like discrimination. It can be used in real-time video surveillance because of its time efficiency and robustness.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-cvi.2018.5324

Support vector machine approach to fall recognition based on simplified expression of human skeleton action and fast detection of start key frame using torso angle

This paper presents a new approach for fall detection based on two features and their motion characteristics extracted from the human torso The 3D positions of the hip center joint and the shoulder center joint in depth images are used to build a fall detection model named the human torso motion model (HTMM) Person’s torso angle and centroid height are imported as key features in HTMM Once a person comes into the scene, the positions of these two joints are fetched to calculate the person’s torso angle Whenever the angle is larger than a given threshold, the changing rates of the torso angle and the centroid height are recorded frame by frame in a given period of time A fall can be identified when the above two changing rates reach the thresholds By using the new feature, falls can be accurately and effectively distinguished from other fall-like activities, which are very difficult for other computer vision-based approaches to differentiate Experiment results show that our approach achieved a detection accuracy of 975%, 98% true positive rate (TPR) and 97% true negative rate (TNR) Furthermore, the approach is time efficient and robust because of only calculating the changing rate of gravity and centroid height

A New Approach to Fall Detection Based on the Human Torso Motion Model

Sitting posture has a close relationship with our health, keeping right sitting posture is important for people to avoid chronic diseases. However, automatic unhealthy sitting posture detection system is rare, especially for those based on computer vision technology. This paper proposes a new method of judging unhealthy sitting posture based on neck angle and torso angle detection using Kinect sensor. The method tracks neck angle and torso angle as two representative features from the depth image in a given period of time to judge whether the sitting posture is healthy or not. Experimental results show that the proposed method can judge sitting posture effectively for different unhealthy sitting types. Compared with the existing methods of action recognition, our method only needs a Kinect sensor without any other wearable sensors and is time efficient and robust because of only calculating two angles.

Leiyue Yao

Papers

Detection of Human Falls on Furniture Using Scene Analysis Based on Deep Learning and Activity Characteristics

Support vector machine approach to fall recognition based on simplified expression of human skeleton action and fast detection of start key frame using torso angle

A New Approach to Fall Detection Based on the Human Torso Motion Model

A New Kinect Approach to Judge Unhealthy Sitting Posture Based on Neck Angle and Torso Angle