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

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.

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Elderly Fall Detection Systems: A Literature Survey

Vision-based fall detection systems have experienced fast development over the last years To determine the course of its evolution and help new researchers, the main audience of this paper, a comprehensive revision of all published articles in the main scientific databases regarding this area during the last five years has been made After a selection process, detailed in the Materials and Methods Section, eighty-one systems were thoroughly reviewed Their characterization and classification techniques were analyzed and categorized Their performance data were also studied, and comparisons were made to determine which classifying methods best work in this field The evolution of artificial vision technology, very positively influenced by the incorporation of artificial neural networks, has allowed fall characterization to become more resistant to noise resultant from illumination phenomena or occlusion The classification has also taken advantage of these networks, and the field starts using robots to make these systems mobile However, datasets used to train them lack real-world data, raising doubts about their performances facing real elderly falls In addition, there is no evidence of strong connections between the elderly and the communities of researchers

/pdf/comprehensive-review-of-vision-based-fall-detection-systems-39ys2dws81.pdf

Comprehensive review of vision-based fall detection systems

The existing deep learning methods for human fall detection have difficulties to distinguish falls from similar daily activities such as lying down because of not using the 3D network. Meanwhile, they are not suitable for mobile devices because they are heavyweight methods and consume a large number of memories. In order to alleviate these problems, a two-stream approach to fall detection with the MobileVGG is proposed in this paper. One stream is based on the motion characteristics of the human body for detection of falls, while the other is an improved lightweight VGG network, named the MobileVGG, put forward in the paper. The MobileVGG is constructed as a lightweight network model through replacing the traditional convolution with a simplified and efficient combination of point convolution, depth convolution and point convolution. The residual connection between layers is designed to overcome the gradient disappeared and the obstruction of gradient reflux in the deep model. The experimental results show that the proposed two-stream lightweight fall classification model outperforms the existing methods in distinguishing falls from similar daily activities such as lying and reducing the occupied memory. Therefore, it is suitable for mobile devices.

/pdf/a-two-stream-approach-to-fall-detection-with-mobilevgg-23olyt73cu.pdf

A Two-Stream Approach to Fall Detection With MobileVGG

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

Adversarial learning with data selection for cross-domain histopathological breast Cancer segmentation

Objective To compare of the efficacy and safety of neuroendoscopic endonasal transsphenoidal surgeries and intracranial endoscopic pterional approach in resection of tuberculum sellae meningioma. Methods From January 2014 to June 2021, 60 patients with tuberculum sellae meningioma diagnosed and treated in our hospital were enrolled and randomly divided into study group and control group. The tuberculum sellae meningioma was removed by neuroendoscopic endonasal transsphenoidal surgeries in the study group, while the intracranial endoscopic pterional approach was used in the control group. The chi-square test was used to compare the differences of tumor complete resection rate, visual acuity improvement rate, total effective rate at 3 months after operation, and adverse reactions between the two groups. Results The clinical characteristics of the two groups were comparable (P > 0.05). After surgical treatment, the complete resection rate in the study group was higher than that in the control group (93.3% vs 70.0%), and the difference was statistically significant (P=0.020). After treatment, the visual acuity improvement rate of the study group was 83.3% (25/30), which was significantly higher than that of the control group (60.0%, 18/30), and the difference was statistically significant (χ2 = 4.022, P=0.045). After surgical treatment, the total effective rate at 3 months after operation was higher in the study group than in the control group (96.7% vs 83.3%), with statistical significance (P=0.041). There was no significant difference in postoperative adverse reactions between the study group and control group (33.3% vs 30.0%, P=0.781). Conclusion The neuroendoscopic endonasal transsphenoidal surgeries has significant efficacy and can significantly improve the visual acuity of patients without increasing adverse reactions, which is worthy of clinical promotion.

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Comparison of the Efficacy and Safety of Neuroendoscopic Endonasal Transsphenoidal Surgeries and Intracranial Endoscopic Pterional Approach in Resection of Tuberculum Sellae Meningiomas

Zhenrong Lin

Papers

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

Adversarial learning with data selection for cross-domain histopathological breast Cancer segmentation

Comparison of the Efficacy and Safety of Neuroendoscopic Endonasal Transsphenoidal Surgeries and Intracranial Endoscopic Pterional Approach in Resection of Tuberculum Sellae Meningiomas

Adversarial learning with data selection for cross-domain histopathological breast Cancer segmentation

Probabilistic Matrix Factorization for Visual Tracking