An Efficient Machine Learning Prediction Method for Vehicle Detection: Data Analytics Framework


The availability of transportation is considered a significant hallmark of a developed society. Since the evolution of the human species, the imperative to relocate from one location to another has been a fundamental requirement. At present, there exists a plethora of transportation options in Indonesia. However, most individuals favor road transportation due to its ease and convenience. The rise in population has led to a corresponding increase in the number of vehicles on the roadways. Hence, it presents a challenge for security authorities and governmental bodies to oversee all automobiles' mobility across various locations effectively. 

The present study proposes a methodology for detecting and tracking vehicles using video-based techniques. The process's initial stages involve preprocessing, including frame conversion and background subtraction. Next, the process of detecting vehicles involves the utilization of change detection and a model of body shape. Subsequently, the next stage entails the feature extraction process, focusing on extracting energy features and directional cosine. Subsequently, a technique for optimizing data is employed on the vector comprising excessively extracted features. The methodology integrates a data mining technique based on association rules, which is subsequently complemented by a random forest classification algorithm. The approach generally integrates multiple methodologies to attain effective and precise identification of automobiles in video-derived datasets.

Traffic disruption is a prevalent issue in Indonesia, particularly in the province of Special Capital District (DKI) Jakarta. The authorities have implemented multiple measures to mitigate traffic disruption in Jakarta. One of these initiatives involves the establishment of the Jakarta Smart City information system. The Jakarta Smart City information system harnesses closed-circuit television (CCTV) data from multiple sources, such as the Transportation Agency (DisHub), Bali Tower, the Public Works Service (PU), and Transjakarta, among others. Around 6,000 CCTVs are distributed across the Jakarta region, with their real-time data being transmitted and displayed on the portal of the Jakarta Smart City system. Quick detection of vehicles becomes necessary to provide inattentive drivers with sufficient time to avoid traveling conflicts and thus minimize the likelihood of rear-end collisions. Moreover, the current techniques for traffic surveillance that count automobiles using electric circuits on the road are costly. All of these factors necessitate the investigation of novel and favored techniques for the vehicle recognition task. Typically, the primary objective of detecting vehicles is to identify potential vehicle positions within an image and designate them as areas of interest (A.O.I.) for subsequent processing tasks. In contrast, computerized automobile identification is a complicated and intrinsically tricky task.

To detect moving vehicles on avenues, reliable systems and programs with efficient extraction methods are required. Real-time traffic inputs produce an enormous volume of data every day; to manage such a large quantity of data, artificial intelligence (A.I.) and computer vision methods are combined to improve the precision of the framework. This recent technological advancement has reduced human and labor needs. A robust video-based surveillance apparatus must be adaptable to the environment's behaviors. However, threats such as trembling cameras and noise interference still exist. Recognizing vehicles during the day is difficult because lengthy reflections cast by the sun can lead to misclassification or interference. In contrast, night vision detection presents difficulties due to the lack of adequate enlightenment, making it difficult for the classifier to identify effectively. Identifying target motion using artificial intelligence (A.I.) technology is one of the foundations of automobile environment sensing. Moving objects in conveyance typically refer to automobiles or individuals available in operating conditions. Additional immobile things, including transportation systems and vegetation, are typically called landscapes. 

To obtain the desired format, it is necessary to distinguish moving components from the background contemporaneously by examining the video input footage extensively. Diverse strategies were employed to establish technologies capable of detecting, counting, and classifying automobiles for use in automated transport platforms' traffic tracking. This section addresses the subject matter of these kinds of systems and an understanding of the methodologies used in creating them. Naz et al. presented a video-based actual time tracking of vehicles using the optimized simulated loop methodology. The researchers utilized real-time traffic monitoring equipment installed along roads to determine the number of vehicles that traveled on the road. In this approach, accounting is done in three stages by monitoring the vehicle's movements throughout an imaginary loop monitoring zone. Ukani et al. presented an alternative video-based vehicle identification approach. In this approach, comparatively high-mounted observation cameras were employed for collecting a roadway video feed; the Adjustable framework estimating, and the Gaussian shadowing reduction consisted of the two primary techniques used. The system's precision depends on the viewing angle and its capacity to eliminate shadowing and phantom effects.

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