Behavior Analytics recognizes situations of risk early on by detecting specific human postures: for example, an individual lying down, a worker’s raised arms. Handrail holding detection helps in labor safety enforcement at production facilities, construction sites, working at height, etc.

When applied to the Object Tracker, the neural network accurately detects specific types of moving objects, e.g. humans or vehicles. This technology can filter out false alarms in busy scenes where multiple moving objects might interfere with the results. You can apply any conventional video analytics (loitering, line crossing, object appearance and disappear- ance, etc.) to the detected objects.

Intelligent fire and smoke video detection operates in areas where other types of sensors are ineffective, e.g. in open spaces. It provides early detection of fire hotspots, which leads to a significant reduction in asset damage.

Plum captures and recognizes human faces and vehicle number plates. You can quickly check a person’s photo or a vehicle number, full or partial, against the video footage. Multiple camera search is also possible.

Our AI algorithms are trained to detect anomalies and potential threats instantly by analyzing live video feeds with unparalleled accuracy. Whether it’s identifying unauthorized access attempts, detecting suspicious behaviors, or recognizing safety hazards like equipment malfunctions or spills, our AI ensures that threats are promptly identified and addressed, mitigating risks before they escalate.

Moreover, Plum’s AI-driven CCTV systems are designed to automate monitoring tasks that traditionally require intensive human oversight. This automation not only reduces operational costs but also ensures round-the-clock surveillance without fatigue or oversight. AI can monitor large areas and multiple cameras simultaneously, alerting our operators to any unusual activities or breaches of security protocols promptly

A significant advantage of Plum’s AI technology is its ability to learn and adapt over time. Through machine learning algorithms, our systems continuously improve their detection capabilities based on data feedback and new insights. This adaptive learning process enables us to stay ahead of evolving security threats and adapt our monitoring strategies to suit the specific needs of each industrial environment.

Furthermore, our offsite monitoring capability means that security personnel do not need to be physically present on-site to maintain vigilance. This remote monitoring setup not only enhances convenience but also allows for rapid response times in emergencies, as our operators can alert onsite personnel or emergency responders immediately upon detecting a threat.