In the United Arab Emirates, the Surveillance security services company in Dubai is committed to offering reliable security services with a smile. We provide services based on the most recent security technology that are guaranteed to fit your requirements and budget. Our goal is to offer our clients high-quality solutions that effectively reduce their risk exposure. We take great pride in our ability to offer a variety of services that are unmatched in Dubai, United Arab Emirates.
Security and surveillance service has uniformed, trained, deployed, and supervised security guards for every scenario conceivable, screening guards and disseminating knowledge that has been demonstrated in the industry.
To provide unmatched value and service to our customers throughout the UAE, we use our expertise in security management and market trends. building dependable relationships founded on shared values, delivering high-quality services, and ensuring safety.
By encouraging innovation and giving our employees the tools they need to excel, we will accomplish excellence. With the help of our specialized tracking technology, our team of experts can create a plan, dispatch a qualified security team to your sites with detailed, personalized post orders, and provide you with information on shift coverage.
- professionally educated and trustworthy
- full-service, round-the-clock emergency response
- completely SIRA-licensed
CCTV camera installation and use for security and monitoring are obvious choices. Cameras are regarded as a basic component for constructing any surveillance infrastructure, but operators cannot provide proactive surveillance and prompt reaction to breaches by continuously watching hundreds or thousands of video feeds.
Although software-based Video Content Analytics (VCA) raises real-time alerts for a few common breaches like left luggage, motion detection, etc., the inaccuracy and false positives far outweigh the potential benefits, to the point that most operators disable these analytics to avoid the countless false alarms.
By exposing them to many tagged examples, Deep Neural Networks (DNN) and Artificial Intelligence (AI)-enabled VCA software are being taught to recognize, identify, and distinguish different objects in video. Aside from AI-based object classification, computer vision algorithms are also used to extract information such as absolute speed and size, orientation, color, path, and area. The video analytics effort can then be focused on information that is pertinent by searching this data.
Video Content Analysis
A structured database of information is produced from the unstructured video data by the VCA software by frame-by-frame analysis of the video feed. The raw video feed is taken in by the VCA engine, which transforms it into a format that can be understood. Utilizing computer vision and deep learning technology, it then analyzes the same. The following vital duties are carried out as part of this processing:
- Discovery of Objects
- Division of Objects
- Finding objects
- Finding objects
- Classes of Objects
Along with the operations, the metadata includes extracts of the different object attributes, such as timestamp, color, and size. For greater precision, deep learning classification and recognition algorithms are used in this case. After that, this metadata is prepared for different analytics.
Law enforcement organizations place a high priority on precise face detection and recognition. It is useful for both pre-incident inquiries and for locating persons of interest. A few advantages of facial recognition software are:
- automatic presence
- Re Identification of unknown persons or
- automatic recognition of authorized individuals
- Automatic warnings for violating no-go zones or being on a blacklist or being banned
- reports on alerts, movements, area access, and area utilization that can be customized
Using digital images extracted from the video, outside image sources, and predefined watchlists, precise face recognition quickly locates individuals of interest in real time. To create a feature vector that depicts a particular face, distinct face features are extracted and encoded. When a face search is performed, this feature vector from the database is used to match the face to the watchlist.
Deep neural network (DNN) models with a large number of sample faces can now be used to teach FR Systems thanks to the advancement of AI-based deep learning algorithms. A large-scale, real-time implementation of facial detection is now possible thanks to the development of GPU technology.
During post-incident analysis, object tracking makes it easier to find a car in the event of a hit-and-run accident or to find a person who might have left a suspicious package at the scene. Once an item has been identified and segmented in a frame using computer vision algorithms, it can then be compared to a list of predetermined categories, such as a vehicle, bike, truck, or person wearing a hat, jacket, or backpack. To recognize these groups, the VCA software can be trained using DNN models. Once the object of interest has been identified and matched, the object segmentation determines the pixels that the object uses, and the movement of those pixels across the video frames can be followed by numerous CCTV Cameras, providing the object’s entry and exit points.
The next phase of video analytics is artificial intelligence. Deep learning-based AI techniques are now extensively used by many VCA software OEMs due to the introduction of high-performance GPU hardware. This raises detection precision without exponentially raising hardware costs. Identifying unusual incidents and resolving many video forensic issues, significantly reduces the workload of security personnel for end users and offers significant advantages. They can also use the enormous quantity of CCTV video data generated for system training rather than having it gradually overwritten. The identification process will get better in the future, which will increase the use of AI in security and surveillance.