Efforts to Prevent Railway Suicides in Denmark

Background: Reviews of camera surveillance systems have demonstrated ambivalent behaviors among people who die by railway suicide. Yet, only few preventive measures have been evaluated. Aims: We aimed to review incidents of suicidal behavior at a Danish railway station, install preventive measures, and monitor subsequent calls to a telephone helpline and reports of suicidal incidences. Method: Suicide incidents at Valby Station during 2012–2018 were reviewed to identify options for preventive measures. Based on these findings, signs encouraging help-seeking and other measures were implemented. Calls to the Danish helpline for suicide prevention and suicidal events at the station were subsequently monitored. Results: The review revealed locations where measures were meaningful and signs, physical barriers, and motion-sensitive lights were installed. Over the following 14 months, no suicide deaths occurred, and the signs were mentioned in 14 calls to the helpline, some of which were made by callers who were evaluated to be at high risk of suicide. Limitations: No direct link between implemented measures and observed outcomes could be established. Conclusion: Installing measures, including signs, at appropriate locations at railway platforms may encourage people in crisis to seek support.

Transformers in Pedestrian Image Retrieval and Person Re-Identification in a Multi-Camera Surveillance System

Person Re-Identification is an essential task in computer vision, particularly in surveillance applications. The aim is to identify a person based on an input image from surveillance photographs in various scenarios. Most Person re-ID techniques utilize Convolutional Neural Networks (CNNs); however, Vision Transformers are replacing pure CNNs for various computer vision tasks such as object recognition, classification, etc. The vision transformers contain information about local regions of the image. The current techniques take this advantage to improve the accuracy of the tasks underhand. We propose to use the vision transformers in conjunction with vanilla CNN models to investigate the true strength of transformers in person re-identification. We employ three backbones with different combinations of vision transformers on two benchmark datasets. The overall performance of the backbones increased, showing the importance of vision transformers. We provide ablation studies and show the importance of various components of the vision transformers in re-identification tasks.

Improving Performance During Camera Surveillance by Integration of Edge Detection in IoT System

This paper is proposing an IoT-based camera surveillance system. The objective of research is to detect suspicious activities by camera automatically and take decision by comparing current frame to previous frame. Major motivation behind research work is to enhance the performance of IoT-based system by integration of edge detection mechanism. Research is making use of numerous cameras, canny edge detection-based compression module, picture database, picture comparator. Canny edge detection has been used to minimize size of graphical content to enhancing the performance system. Simulation of output of this work is made in MATLAB simulation tool. Moreover, MATLAB has been used to give comparative analysis among IoT-based camera surveillance system and traditional system. Such system requires less space, and it takes less time to inform regarding any suspicious activities.

Smart Home Technologies

This project describes about smart home concept which is made up using 2 microcontrollers. this project assures complete and proper utilisation of IOT with three application-camera surveillance(home security)-smart plant caring systemhome automation

IP CAMERA SURVEILLANCE SYSTEM USING ANDROID APPLICATION BASED ON ARDUINO

Keamanan menjadi fokus utama bagi setiap orang, terutama bagi mereka yang memiliki barang berharga. Kamera pengintai merupakan salah satu cara untuk mencegah terjadinya pencurian. Sistem pengawasan telah dirancang dengan menggunakan IP kamera dengan fitur Motion Detection di dalamnya. Fungsi kamera IP adalah untuk mendeteksi dan menangkap gambar dari setiap gerakan dengan menggunakan fiturnya dan mengirimkan snapshot ke situs pengguna melalui FTP (File Transfer Protocol). Selain itu, sensor PIR (Passive InfraRed) yang digunakan sebagai motiondetectorakan dihubungkan ke mikrokontroler Arduino uno dan ethernet shield. Sistem ini juga menggunakan aplikasi pada smartphone android sebagai alert. Jika sensor mendeteksi beberapa gerakan, sensor akan mengaktifkan notifikasi di aplikasi android. Notifikasi di aplikasi android berisi 'Motion Detected check ftp: //ftp.roomsurveillance.info'. Hasil dari sistem surveilans ini berupa snapshot yang dikirimkan oleh kamera IP, dapat dilihat dan diunduh dengan mengklik link FTP. Sistem pengawasan ini dapat meningkatkan keamanan ruangan dalam kondisi realtime.

A Review On State Of The Art Abnormal Activity Recognition Approaches

In last few decades, technological revolution has accelerated the deployment of large scale surveillance systems on almost all public places such as malls, hospitals, airports, railways, bus stations, roads, etc. These intelligent surveillance systems can play crucial role in governance of situations, collective security and safety, mitigating as well as prevention of adversaries. With gradual increase in multi camera surveillance systems enclosing multi angle views of same as well as different scenes has increased complexity of monitoring the systems by manual inspection. Abnormalities also known as anomalies or outliers are inevitable part of the existence and presumed to be rare in occurrence. Manual monitoring of such abnormalities is susceptible to errors and limited by human capabilities such as inattention and tiresome. Hence in the field of computer vision, automated abnormal activity recognition (AAR) from surveillance systems is emerging research area. The intent of this research is to shed a light on recent innovations and developments that have made a mark in abnormal activity recognition (AAR) involving deep learning. This paper also includes conventional categorization of anomalies based on different perspectives which can provide better understanding to young researchers. Though recent developments still poses many real time challenges in automatic abnormal activity recognition, some of them are enclosed in this paper.

Malleefowl Leipoa ocellata breeding behaviour: Insights from citizen science camera surveillance

Observations on natural history are a useful but often overlooked branch of ecology. With the use of camera-traps, collection of data e.g. on breeding behaviour and success has never been easier, particularly when studying cryptic species. Additionally, camera-traps are well suited to citizen science because of their ease of operation and the ability to store and verify data. Malleefowl Leipoa ocellata breeding behaviour has previously been studied through direct observation or timelapse photography, resulting in small sample sizes and potentially biased observations. Using camera-trap data collected by citizen scientists, we examined the breeding behaviour of this species. We quantified the timing and duration of mound-tending activities at 20 mounds using camera surveillance for >30,000 hours (1250 days) over six breeding seasons. Time spent at the mound during five mound stages and visit frequencies by the male and female during each stage are reported. Femaleinvolvement at the mound was consistent, although males spent three times as long at the mound compared with females during egg-laying. On egg-laying days, females spent longer uncovering the mound, compared with covering the mound post egg-laying. Our findings confirm that both male and female Malleefowl spend a substantial amount of time constructing and maintaining their mound throughout the year and, most notably, that the female consistently participates in mound-tending.These insights are particularly valuable as our surveillance was over markedly longer time spans than previous studies that relied upon direct observation.