Smart Stables: Revolutionising Horse Care with AI

Artificial Intelligence is revolutionising the care of horses in stables, providing advanced tools to enhance well-being and management. Utilising AI-driven video analytics, stables can implement systems that continuously monitor horses for signs of distress, illness, or injury, enabling early detection of unusual behaviours that might indicate a problem. Machine learning algorithms analyse video data in real time, identifying deviations from normal activity, such as limping (suggesting potential injuries), cribbing (indicative of stress or discomfort), or changes in eating and sleeping patterns. This technology allows for immediate interventions, improving outcomes and potentially saving lives. The application of AI in equine management not only enhances individual horse care but also promotes higher standards of welfare across the industry, making it possible to maintain consistently high levels of health and safety within equine facilities.

IntelexVision’s iSentry, an AI-powered platform could dramatically improve equine management by providing enhanced monitoring capabilities within stables. Adapted to the unique requirements of horse care, iSentry’s technology offers real-time video analysis to detect unusual behaviour, health issues, or signs of distress in horses efficiently. It enables caretakers to respond promptly to the needs of each horse. This proactive approach not only ensures the well-being and safety of the horses but also optimises the allocation of resources and improves the overall management of equine facilities.

Managing a horse stable presents a myriad of challenges, from ensuring adequate ventilation to prevent respiratory issues, to maintaining optimal space and cleanliness to support the horses’ health and well-being. One significant issue is the monitoring of horses to quickly identify and respond to signs of illness, injury, or distress.

Here, AI video analytics can play a transformative role. By integrating AI-powered monitoring systems like iSentry into stable operations, stable managers can leverage real-time video analysis to detect unusual behaviours or health anomalies with precision and speed.

This not only enhances the ability to intervene early, potentially saving lives and reducing veterinary costs, but also improves overall management by monitoring trends and patterns that inform better care practices. Additionally, such technology can help optimise the environment, from adjusting light and sound to better suit the horses’ needs, thereby adding immense value to traditional horse care and management strategies. Integrating iSentry into horse stable operations enhances the capability to detect unusual behaviours such as pacing, excessive lying down, or signs of colic with a high degree of accuracy.

The system analyses real-time video feeds, applying machine learning algorithms to distinguish between normal and potentially worrying behaviours. This allows for immediate alerts to be sent to stable managers and caretakers when abnormal behaviour is detected, facilitating swift action to address possible health or welfare issues.

With iSentry’s continuous monitoring, the management can achieve a more proactive approach to health care, reducing the incidence and severity of issues that might otherwise escalate undetected.

iSentry from IntelexVision can address several critical use cases in the stabling, enhancing safety and operational efficiency. Here are some specific applications:

Cribbing/Windsucking: This involves the horse grabbing onto a solid object such as the edge of a stall with its teeth and sucking in air. It can be indicative of stress, boredom, or gastrointestinal discomfort.

Weaving: A horse may sway side to side repetitively, usually by shifting its weight from one front foot to the other. This behavior is typically associated with stress, confinement, or lack of exercise.

Kicking Walls: If a horse frequently kicks the walls of its stall, it may be experiencing irritation, discomfort, or stress. It can also be a sign of pain or aggression.

Biting or Charging at Stall Bars: Aggression towards people or other horses passing by can indicate stress, protection of resources, or social issues.

Excessive Lying Down: While horses do lie down to rest, excessive lying down or reluctance to stand can be a sign of illness, pain, or weakness.

Self-Mutilation: While rolling is normal in some contexts, doing it excessively or having difficulty getting up can be indicative of abdominal pain or colic.

When any of these behaviours are noticed, it’s important to evaluate the horse’s environment, diet, and health, consulting a veterinarian or an equine behaviorist.

iSentry is an Artificial Intelligence-powered video analysis platform. It can be installed on new systems as well as the vast majority of existing CCTV systems.

Equipped with the latest Artificial Intelligence (AI) and Neural Networks based Machine Learning algorithms, iSentry quickly learns what is normal from an individual CCTV camera feed, so that it can then detect the abnormal. It can be deployed in systems from just a few cameras up to thousands of cameras.

After a norm is established for a particular scene, the system will then create alerts based on exceptions or ‘events of interest.’ iSentry then classifies each event upon detection using Deep Learning tools and a logic engine. This provides instant situational context to control room operators so that they can better understand what they are being shown, allowing them to respond appropriately.

iSentry also works in animal behaviour analysis, e.g., horses, enabling better care and safety by detecting any unusual behaviour which could be a sign of illness or stress.

iSentry detects loitering, directional violation, unusual objects entering a scene, running, violence, tailgating, smoke and fire, major leakages, removed or introduced static objects, people climbing walls, entering a perimeter or

area, or graffiti/signs painting. iSentry can also carry out pose analysis, (whether someone is standing, sitting, lying on the ground or has fallen) and many other abnormal situations.

Its Deep Learning engine recognises multiple classes of objects even at difficult angles. Additional capabilities have recently been added such as the ability to monitor for Health & Safety compliance, for example, the wearing of hard hats, high visibility jackets, eye protection glasses, face shields or facemasks for COVID compliance.

iSentry´s powerful Logic Engine can largely and autonomously fulfil the function of a video surveillance operator. More than 80% of the time it will be capable of reaching a correct decision regarding an event of interest, based on the number and combination of object types that trigger an alert, the time of day and object size, or even the likelihood of accurate classification.

Any incident that iSentry cannot confidently classify automatically or determine through the rules engine whether to dismiss or alarm is then transferred to a human operator for further investigation and decision-making. iSentry empowers control room operators to solely focus on those decisions at which humans excel. iSentry also enables control rooms to function effectively with far fewer operators, as massive quantities of video can be meaningfully and accurately monitored, and processed by the platform.

To reduce the number of false alarms many Video or Alarm Monitoring Centres use Video Verification Platforms to determine if there are people or vehicles in specific snapshots of alerts, typically generated by on-camera motion detection analytics. This can lead to a reduction in overall alarms by as much as 50-70%.

While this sounds impressive, control room operators are still left with an insurmountable quantity of false alarms to deal with on every shift.

The iSentry platform uses AI to first detect and then intelligently classify alerts. As a result, the number of alerts control room operators receive is 90-95% lower than standard video verification systems. With iSentry, fewer operators can perform more, and to a much higher standard, freeing personnel to work on other higher value-added tasks.

We tested iSentry’s Unusual Behaviour on a 25-camera system against traditional video verification software platforms. iSentry escalated 2,086 alerts to operators, against 32,616 alerts from standard video verification software. iSentry also reduced escalated operator alerts by 94% greatly increasing efficiency and lowering alert-related operational costs.

In a major city, iSentry monitors many thousands of cameras in real-time. Our system’s AI and Deep Learning powered alert detection has become so precise that our system now only presents 1% of total recorded video to a control room operator for review.

The accuracy of their recall of events of interest flagged by iSentry is 90%+. Prior to our system being installed, this was between just 5% and 10%.

Prior to iSentry’s installation, one control room operator was responsible for managing up to 80 cameras with very poor incident detection performance. With iSentry, the same operator can monitor a daily average of 350 cameras and up to 800 cameras at off-peak times.

iSentry was installed by a major bank across hundreds of branches with an average of 30 CCTV cameras per branch. As a result the total amount of video to be reviewed was reduced to less than 1% of the previous quantity, without missing any event of interest.

This significant reduction in video footage and the efficient hardware and software architecture of iSentry has led to very low bandwidth demands on the central Control Room together with a reduced need for operator consoles. This has led to major CAPEX savings.