Violence and Danger

Crime has become one of the major problems in todays' society, visual surveillance based solely on human supervision is less efficient and accurante in detecting dangerous situations. The use of deep learning can help alert on violent and dangerous behaviors and avoid public disorder.

Mask Detection

Mask Face Detection is an important protective equipment approach for combating the propagation of the coronavirus. Computer vision systems can substantially aid in the monitoring of compliance and adherence to mask use. It identifies and inspects video footage to improve efficiency and safety while reducing costs. The use of vision-based control is extremely scalable across several sites.

Traffic Monitoring

Traffic management centers monitors a plethora of distributed cameras accross their network. It is reported that more than 1 billion of CCTV are deployed accross the globe. Manual traffic surveillance can become complex and resources intensive. This challenging task needs the contribution of artificial intelligence to optimize the way surveillance is conducted in our cities. Deep learning models could contribute to better manage traffic and reduce congestion.

Parking Lot Occupancy

Parking lot can use multiple traffic related issues, as its occupancy can not be determined by a person simply looking for an empty spot. The use of a visual parking lot occupancy detection that uses deep learning alghorithms can make processing for public and private parking facilities decentralized. It can detect and classify vehicles in parking spots and check in real time their availability. This will lead to parking lot optimization and reducing traffic congestion during peak hours and traffic flows in cities.

Waste Sorting

The recent enviromental challenges have put into question traditional ways of waste processing. Revaluing and sorting waste can be possible with the use of AI. Users can install a smart camera with visual recognition that automatically detects non-compliant objects. It is a useful tool for process optimization since it eliminates burner shutdowns, resulting in considerable cost savings.