# Highly Accurate Deep Learning Models for Estimating Traffic Characteristics from Video Data
## 2 Literature Review
### Video-Based Computer Vision Detection Methods  
Video footage-based detection methods manifest superiority in terms of financial cost and hardware efficiency. Video-based detection algorithms, especially online-compatible ones, are trivial to integrate into pre-existing traffic surveillance cameras on highways, negating all external hardware costs. With the prevalence of existing traffic cameras (estimated at 24 million installations in China, though <1% of these have smart monitoring algorithms implemented) [1], incorporating automatic detection methods relieves dependence on manual monitoring, yielding a significant increase in the efficiency of each installation. In addition, sensor-based approaches are more limited in their range of applications and are often restricted to carrying out inference/analysis regarding one particular aspect. For instance, radar-based accident prevention approaches are limited to distance evaluation only [19]. Video-based detection methods enable more conclusive analysis of the collected region, such as abnormality identification (e.g., traffic accidents, incorrectly parked vehicles, off-lane vehicles), optic flow tracking of vehicles, and traffic density analysis. Multiple sensor installations, each dedicated to one of the above tasks, are not needed due to the versatility of video-based detection [20].  
Previous studies have explored the benefits of video-based analysis algorithms and their application potential. [PERSON] et al. introduced a simple online vehicle contour-based density measurement method [21]. [PERSON] et al. proposed a traffic camera-oriented density detection approach based on the Haar cascading algorithm within a designated region of interest (ROI) [8]. Deficiencies of vision-based analysis methods, such as small object detection and environmental influence, have been addressed and mitigated in various vision-only detection studies [22]. [PERSON] et al. developed a traffic accident detection method based on neural networks and cooperative vehicle infrastructure systems (CVIS) [23]. By incorporating multi-scale feature fusion in the proposed YOLO-CA network, the model yields an improved ability to detect smaller/more distant objects, addressing the deficiency of small object detection common to vision-based object detection methods. [PERSON] et al. extracted traffic flow from low-resolution surveillance cameras using a fine-tuned Detectron2 model [24]. [PERSON] et al. addressed vision-based detection in low-visibility conditions by utilizing retinex image enhancement to minimise the impact of severe weather conditions on detection results [25]. The enhanced image input undergoes object detection and determines the accident probability via a decision tree. [PERSON] et al. showed the feasibility of vehicle-based autonomous detection methods (as opposed to implementing detection by traffic cameras) and designed a first-person risk evaluation system based on anomaly scores derived from motion inconsistencies of preceding vehicles [26]. [PERSON] et al. utilised the comprehensive information provided by cameras to devise a two-stream convolutional network architecture to perform near-accident detection [27].