Video Data Extraction and Processing for Investigation of Vehicles’ Impact on the Asphalt Deformation Through the Prism of Computational Algorithms

Abstract

There are numerous algorithms and solutions for car or object detection as humanity is aiming towards the smart city solutions. Most solutions are based on counting, speed detection, traffic accidents and vehicle classification. The mentioned solutions are mostly based on high-quality videos, wide angles camera view, vehicles in motion, and are optimized for good visibility conditions intervals. A novelty of the proposed algorithm and solution is more accurate digital data extraction from video file sources generated by security cameras in Bosnia and Herzegovina from M18 roadway, but not limited only to that particular source. From the video file sources, data regarding number of vehicles, speed, traveling direction, and time intervals for the region of interest will be collected. Since finding contours approach is effective only on objects that are mobile, and because the application of this approach on traffic junctions did not yield desired results, a more specific approach of classification using a combination of Histogram of Oriented Gradients (HOG) and Support Vector Machines (Linear SVM) has shown to be more appropriate as the original source data can be used for training where the main benefit is the preservation of local second-order interactions, providing tolerance to local geometric misalignment and ability to work with small data samples. The features of the objects within a frame are extracted first by standardizing the feature variables and then computing the first order gradients of the frame. In the next stage, an encoding that remains robust to small changes while being sensitive to local frame content is produced. Finally, the HOG descriptors are generated and normalized again. In this way the channel histogram and spatial vector becomes the feature vector for the Linear SVM classifier. With the following parameters and setup system accuracy was around 85 to 95%. In the next phase, after cleaning protocols on collected data parameters, data will be used to research asphalt deformation effects.

AI-Enabled Assessment of Roadway Integrity: Forecasting Bitumen Deformation and Road Stability Throughout the Lifecycle Under Traffic Impact

“…This study analyzed the data with a backpropagation algorithm to determine the impact of traffic flow on asphalt deformation. Findings indicated that traffic flow significantly affected asphalt deformation, and the model had good accuracy in predicting the deformation [9,10]. Finally, references [11,12] studied the effects of vehicle speed on rutting deformation of asphalt pavement using real-time traffic data.…”

“…First source is a video document source using road surveillance cameras with a 15-day period, which was used for sampling. A linear support vector machine model extracts vehicle number, class, speed, heading, and time from the recorded footage [10]. The weather and temperature factors are not considered as the measurements and modelling is performed on nearby lanes.…”

“…The weather and temperature factors are not considered as the measurements and modelling is performed on nearby lanes. Figure 2 depicts the detection of on-road vehicles in an area of interest, a key data collection point [10]. HOG and SVM gather data in the region of interest.…”

Tradeoff of Computation Bits and Computing Speed in an Edge Computing System for Sensor Networks

“…We consider block fading channel in this paper i.e., during each T, the channel remains static. Similar to [24][25][26], it is assumed that the wireless channel between the UAV and ground users is dominated by line of sight (LoS) link, hence the channels between the UAV and ground users are modelled by the free space path loss model. Thus, the channel power gain between the UAV and the mth user is denoted by h m ½n, given as:…”

Low-Light Image Enhancement and Target Detection Based on Deep Learning

“…At present, many computer vision systems are installed in outdoor environments and lowtemperature water environments. Their performance is easily affected by light distribution [18][19][20][21][22][23][24]. To complete different vision tasks, e.g., object detection, object recognition, and object retrieval, low-light images must be enhanced by different methods to achieve different processing effects.…”