# Highly Accurate Deep Learning Models for Estimating Traffic Characteristics from Video Data
## 2 Literature Review
### Camera Calibration and Object Tracking Algorithms  
Slow inference speed and expensive computation cost are two significant drawbacks of vision-based approaches that have been notably alleviated by incremental enhancements. [PERSON] et al. presented a novel approach for estimating traffic speed using a sequence of images from an uncalibrated camera by utilizing geometric relationships inherent in the image, using frame differencing to isolate moving edges and track vehicles between frames and using parameters from the distribution of vehicles lengths [8]. [PERSON] et al. assembled a predictive visual accident detection model to minimise the delay from network inferencing speed and evaluated the model with the nearest neighbour and regression tree, two less resource-demanding alternatives [28]. [PERSON] et al. addressed the aforementioned difficulties by selectively dropping frames based on the histogram difference of consecutive frames to restrict unnecessary/similar frames and predict accidents with a support vector machine after feature extraction with ResNet50 and K-means clustering [29]. [PERSON] et al. utilised the mean-shift algorithm as a computationally cheap method of tracking and evaluating vehicle motion to produce accident predictions. The Gaussian mixture model was incorporated to perform vehicle identification [30].  
The aforementioned studies indicate that conventional image processing algorithms often outperform neural network-oriented algorithms in terms of speed and computation cost in a generic setting. To address the neural network's speed deficiency in a real-time prediction scenario while maintaining accuracy, novel iterations of object detection algorithms have been proposed specifically to address online applications.  
[PERSON] et al. proposed a network architecture, You Only Look Once (YOLO), for object detection and identification, which manifests significant computational time advantages in comparison to past detection models, such as sliding-window or region-based algorithms, due to its one-pass detection capability [31; 32; 33]. For this reason, YOLO is commonly integrated into vehicle-tracking models with performance demands as the object detector. YOLO integrated with deep simple and online real-time tracking (SORT) is the most common pair for object tracking [33]. The integrated procedure is composed of two stages: object detection via YOLO and object tracking via deep SORT. Although YOLO performs quickly in video object recognition, when processing object tracking tasks, the deep SORT stage takes time to re-ID objects by calculating their similarities, which improves the overall processing speed.  
Multi-object tracking is an important task to complete in computer vision [7]. Compared to Deep SORT, in which the re-ID task is heavily influenced by the detection stage, FairMOT jointly optimises both the detection and re-ID stages, which is based on the anchor-free object detection architecture known as CenterNet [7].  
Camera calibration for calculating actual speed from video processing has been studied by many scholars. [PERSON] used road lanes, height, and the tilt angle of the camera to calibrate world coordinates in ground distance [34]. [PERSON] and [PERSON] calibrated cameras by using the vanishing point of the road edge to compute the focal length and pan angle of the camera [35]. However, general camera calibration methods assume camera height and tilt angle beforehand or require special calibration patterns in traffic scenarios that are hard to find [10]. [PERSON] et al. developed a camera calibration method by bridging the relationship between the image coordinates to the world coordinates in terms of pan angle, tilt angle, swing angle, focal length, and camera distance [36]. With these parameters, their study proposed a transformation matrix to convert image coordinates to world coordinates [36]. Our paper refers to [PERSON] et al. for calibrating the camera in a typical scene and calculating vehicle speed from its image locations returned by modified FairMOT [36].