1) Vehicle classification

Deep learning methods have made it possible to analyze traffic on a large scale using affordable security cameras. With the rise of inexpensive sensors like CCTV cameras, LiDAR, and thermal imaging devices, vehicles can now be detected and categorized across multiple lanes simultaneously. Combining different sensors improves accuracy. Specialized applications, such as using deep learning for construction vehicle detection, serve various purposes like safety monitoring and productivity assessment. These advancements represent significant progress in transportation technology, promising better traffic management.

2) Traffic analysis and parking detection

With the advancement of computer vision and AI, video analytics can now be applied to common traffic cameras, offering significant benefits to Intelligent Transportation Systems (ITS) and smart city initiatives. Parking space monitoring is crucial for detecting parking lot occupancy, especially in smart cities. Computer vision applications provide efficient solutions for this purpose. Video-based parking management systems, employing stereoscopic imaging or thermal cameras. Camera-based parking lot detection offers scalability for large-scale use, low-cost maintenance, and easy installation, particularly when reusing existing security cameras.

3) Automated number plate recognition

ANPR plays a crucial role in modern transportation and public safety systems. It involves identifying and extracting license plate information from images or videos. ANPR has greatly impacted industries like public safety and transportation, enabling cost-effective tolled roadway solutions and introducing innovative capabilities like police cruiser-mounted license plate reading units.

4) Pedestrian and traffic sign detection

Pedestrian Detection is vital for ITS and serves various purposes such as enhancing self-driving cars, monitoring infrastructure, managing traffic, ensuring transit safety, and aiding law enforcement. It utilizes different sensors like CCTV, thermal imaging devices, and RGB cameras to detect pedestrians. Detection algorithms may rely on infrared signatures, shape features, gradient features, machine learning, or motion features.

Traffic sign detection is another important application of Computer Vision in transportation. It involves segmenting traffic signs from different scenes using image segmentation and then using deep learning algorithms for recognition and classification.

Collision avoidance systems are crucial components of advanced driver assistance systems in autonomous vehicles. They involve detecting other vehicles and identifying lanes to prevent accidents. Recently, deep neural networks have been explored for enhancing collision avoidance systems.

Driver attentiveness detection is aimed at identifying distracted driving behaviors like daydreaming or cell phone usage, which contribute significantly to road traffic fatalities worldwide. Artificial intelligence is employed to analyze driving behaviors and develop strategies to reduce accidents. Road surveillance technologies are utilized for detecting violations inside the vehicle, such as seat belt non-compliance. In-vehicle monitoring systems focus on visually assessing the driver’s attentiveness through inward and outward-facing cameras or sensors. Techniques like gaze direction analysis and facial expression monitoring are used to infer driver behavior. Deep learning algorithms can distinguish between attentive and inattentive faces and detect signs of impairment or distraction.