Global 
United States 
What is a Vehicle Detection System?
With uprising advancements in computer vision in every sector, the transportation and traffic monitoring industry has undergone revolution and thorough advancements. One major application of the same is vehicle detection and counting systems. Vehicle counting refers to the process of detecting and logging the count of various types of vehicles that enter or cross a particular area, zone or pass through a defined line. As an extended arm of object detection, these systems present limitless use cases to count on, such as integration of classification algorithms for distinguishing between different types of vehicles like trucks, cars, cycles etc. Other use cases include traffic management, surveillance and re-routing, highway and safety monitoring, urban planning, tracking vehicle of interest, automatic license plate recognition system, real-time traffic, toll, congestion-related information, crowd or pedestrian counting, face detection and alignment and analytics, etc.
In 2022, the global intelligent traffic management and vehicle detection system market was valued at USD 10,423.7 million, which is increasing at a CAGR of 13.8% till 2030. The main drivers of such rapid growth are attributed to growing demand for traffic-related information from drivers, government initiatives for effective traffic, accidents and infrastructure management. Let us delve into the methods, implementations and algorithms of vehicle detection in this blog.
Rising global Vehicle Detection System market size from 2020 to 2030
Overview of Working of Vehicle Counting System
As we are aware, the algorithms and techniques involved in computer vision enable machines to interpret information presented in the physical world. On considering the use case of vehicle detection and counting system, machines can easily analyze video feeds or images captured by vehicle counting cameras and count vehicles. An account of the steps involved in the same are given below:
Image Acquisition
The first and foremost step involved in the working of all such systems is image acquisition which deals with visual data capturing using vehicle counting camera. These cameras are installed at strategic locations, for example, intersections, highways, for smart parking management etc. and are usually enabled with higher resolution and high-quality image capturing parts. This helps in improving the overall accuracy of vehicle detection and counting processes.
Image Preprocessing
This step is mainly concerned with preparing the captured images or video for analysis. It includes noise reduction for removing any irrelevant information or noise from them. In addition to this, the images are enhanced with respect to brightness, contrast and other parameters for better image quality. And finally, image segmentation, in which the image is divided into segments to isolate regions of interest such as vehicles, road etc. which need to be analyzed by the vehicle counting system.
Vehicle detection using vehicle counting camera
Multi-object Detection and Tracking
Computer vision object detection forms the core of vehicle detection and counting systems as it identifies and locates vehicles in the video feed and images. It can be executed using traditional methods such as background subtraction or comprehensive techniques such as deep learning. Next, the objects detected must be counted by tracking the vehicle’s frame-wise movement within the area of interest. We now move forward to understand the technologies that enable the working of object detection as a part of vehicle detection systems:
- Background Subtraction: It is a widely used method and is simpler as compared to deep learning techniques enabling different types of neural networks. It detects moving objects in video sequences by subtracting the current frame from the reference frame or background model for change detection, which is also called foreground detection. A threshold is then applied to binarize the obtained foreground image, which is then subjected to morphological operations such as dilation and erosion for noise removal and gap filling.
- Histogram of Oriented Gradients: HOG, a feature descriptor for object detection counts the occurrences of gradient orientation in certain localized parts of the image. After gradient computation, the image is fragmented into small spatial regions or cells that are used to compute a histogram showcasing the directions of the gradient in every cell, a method known as orientation binning. Adjacent cells are grouped into blocks to normalize the histogram. It is useful to even out the variations in illumination and other small deformations in the image. Ultimately, a sliding window approach and a classifying technique such as Support Vector Machine (SVM) is used for object detection.
- Convolutional Neural Networks: CNNs can learn hierarchical features from images, and portray an example of unsupervised learning, few examples include You Only Look Once (YOLO), Single Shot MultiBox Detector (SSD) and Faster R-CNN. A CNN-based architecture suitable for forming the basis of vehicle counting system is chosen, designed, trained on a labeled dataset of vehicle images like TRANCOS, KITTI etc. which can be produced by image labeling services and is used to obtain inference while detecting new images and videos. The dataset is usually obtained from a high viewing angle for which a distant road surface is considered since object size can change greatly at this angle. When compared to this, a small object moving in a region far away from the road produces results with low accuracy.
Features of KritiKal’s vehicle counting system (TRAZER)
- You Only Look Once: YOLO is an efficient, accurate and fast object detection algorithm. It divides the image into a grid, then predicts the bounding box with confidence scores for each grid cell. Then for each bounding box, it predicts the class probabilities and applies Non-Maximum Suppression to remove duplicate detections.
Report and Alert Generation
After post-processing, that is removal of duplicates and bounding boxes placed extremely close to each other, the process moves forward to report generation which can be obtained in CSV format. Vehicle detection system can generate reports and alerts for traffic flow analysis, traffic congestion, restricted region monitoring, peak hours, vehicles of interest or stolen vehicles and free routes. This helps highway authorities and ministries to strategize on congestion and peak hour related pricing, traffic re-routing and diversion monitoring. Moreover, devising such traffic control strategies can provide development revenue for projects under urban planning. It can assist in pollution mitigation, environmental monitoring, and the impact of traffic congestion on surrounding air quality, thus helping authorities to initiate effective and informed decision making on vehicle count in a particular region.
Untangling the Complexities of Vehicle Counting System with TRAZER
KritiKal’s TRaffic AnalyZer and EnumeratoR (TRAZER) suite is an automated traffic counting and classification software that uses AI models boosted with TensorRT to assist in time-based traffic data collection and analysis catering to heterogeneous traffic conditions. It provides accurate and auditable traffic data irrespective of complexity of traffic conditions. It is an ideal traffic monitoring and border roadway surveillance solution for real-time and offline traffic analysis based on feed from 4 to 5 vehicle counting cameras.
The UI showcases dashboard visualization and detailed web-based downloadable report that contains all vehicle characteristics (color, type, model, class etc. into 11 categories), vehicle flow count and tracking details (direction, speed, parked vehicle, frame-wise trajectory), congestion detection and heat map for traffic patterns, traffic rule violation (speed, helmet, triple rider or two-wheeler occupancy, wrong lane and direction, zebra crossing) etc. for effective traffic diversion and region of interest-based intelligent video analytics (pre-configured or newly marked). The software has helped many urban planners and highway monitoring authorities in executing their respective traffic-related use cases such as vehicular security at local premises, shopping centers, events, international borders, for urban planning etc., thus ramping up the growth of the society.
KritiKal Solutions can develop an efficient vehicle monitoring system instilling added assurance and tranquility to motorway journeys where the user remains fully aware of traffic conditions. It has assisted some major companies and authorities in transforming their businesses via its advanced traffic video processing and vehicle counting system bundled with secured infrastructure and firmware. Please mail us at sales@kritikalsolutions.com to know more and avail our services.
Neha Gupta currently works as an Engineering Manager at KritiKal Solutions. With over 8 years of experience and exceptional skills in software project management, Agile methodologies, .NET, React.js, Node.js, C#, SQL Server, Web and mobile development etc., she has helped KritiKal in timely delivery of various projects to some major clients.