In football or soccer, a referee controls the game based on the set rules. The decisions made by the referee are final and can’t be appealed. Some of the decisions, especially after a handball event, whether to award a penalty kick or a yellow/red card can greatly affect the final results of a game. It is therefore necessary that the referee does not make an error. The objective is therefore to create a system that can accurately recognize such events and make the correct decision. This study chose handball, an event that occurs in a football game (Not to be confused with the game of Handball). We define a handball event using object detection and robotic perception and decide whether it is intentional or not. Intention recognition is a robotic perception of emotion recognition. To define handball, we trained a model to detect the hand and ball which are primary objects. We then determined the intention using gaze recognition and finally combined the results to recognize a handball event. On our dataset, the results of the hand and the ball object detection were 96% and 100% respectively. With the gaze recognition at 100%, if all objects were recognized, then the intention and handball event recognition were at 100%.

Football or Soccer is one of the most popular games in the world. “Handball” event in the game is one of the most controversial and important decisions by a single referee. This paper proposes a method to define a “handball event” in a football game by recognizing the hand, ball, and their interaction from a single camera. We trained a model to identify hands and balls using detectron2, then used the vertices of two objects to find out the overlapping situation between these two objects to determine the handball event in a football game. The train and test result of detection was satisfactory with 96% for hand and 100% for the ball respectively.

Current traffic light systems follow predefined timing sequences, causing the light to turn green even when no cars are waiting, while the side road with waiting vehicles may still face a red light. Reinforcement learning can help by training an intelligent model to analyze real-time traffic conditions and dynamically adjust signal lights based on actual demand and necessity. If the traffic light becomes intelligent and autonomous then it can significantly reduce the time wasted everyday commuting due to previously determined traffic light timing sequences. In our previous work, we used fuzzy logic to control the traffic light where the time was fixed but in this paper, the waiting time becomes a variable that changes depending on other road variables like vehicles, pedestrians, and times. Moreover, we trained an agent in this work using reinforcement learning to optimize the traffic flow in junctions with traffic lights. The trained agent worked using the greedy method to improve traffic flow to maximize the rewards by changing the signals appropriately. We have two states and there are only two actions to take for the agent. The results of the training of the model are promising. In normal situations, the average waiting time was 9.16 seconds. After applying our fuzzy rules, the average waiting time was reduced to 026 seconds, and after applying reinforcement learning, it was 0.12 seconds in a simulator. The average waiting time was reduced by 97~98%. These models have the potential to improve real-world traffic efficiency by approximately 67~68%.

In this work, we propose an application of on-road surveillance cameras for traffic light optimization. The experiments consist of two parts one is data collection and another is traffic light optimization. In the data collection part, data were collected from the main road with one conjunction. Vehicles and Pedestrians were detected using the Yolov3 method. For lane detection, the Hough transform was used. After collecting the data traffic lights were optimized by some rules. Moreover, due to lack of data and sensitivity, the Simulation of Urban MObility (SUMO) simulator was also used for experiments. We achieved traffic light optimization by 15 seconds in real-time experiments while in the simulator, the time difference between the normal and optimized situation was 129 seconds to clear all the vehicles.

The main reasons behind the traffic jam and accidents are illegal/double parking, over-speeding, violating signal lights, construction, wrong-way driving, reckless driving, unsafe lane changing, etc. To determines the problems and the solutions, this study proposes a two-step approach. One is data collection and the other is Optimization. In the data collection part, traffic information is obtained from various traffic information units through cameras installed in traffic signals and roads. By analyzing the collected data, the systems can take the next step in the optimization part. In the collected data, it is required to detect vehicles, pedestrians, and lanes. Yolov3 method was used for vehicles and pedestrians’ detection. For lane detection, the Hough transform was used. The main goal of the research is not detecting objects but to determine the intelligent systems which can combine all the collected data and give the optimum solutions to control the traffic signals depending on the situations. The study found that sometimes the vehicles are unnecessarily waiting for the signals. If the unnecessary time could be saved through signals then it would reduce time consumption, oil consumption, and mental impatience. The result would give us opportunities to reduce accidents, pollution, money, and time. Moreover, the systems can measure the speed which helps find out rule violating vehicles. This paper also proposed a method for shortest path calculation. In addition, automatic penalty execution can be carried out through the collected data. For that number plate recognition is included in future works.

Computer vision is an important part of artificial intelligence. It helps machines interpret and make decisions based on visual data. As machines take on more responsibility in decision-making, vision is a key way for them to understand their surroundings. The ability for machines to see and understand the world through visual input raises the question of whether they can truly understand complex situations based on the objects and interactions around them. This paper explores the main concepts and algorithms behind computer vision, beginning with its early development. It discusses foundational techniques and how they have changed over time, including innovations that are shaping the field. The paper also looks at the limitations of these foundational concepts and how they have impacted the growth of current technologies. It includes a critical examination of present-day technologies, pointing out their challenges and shortcomings despite many improvements. Finally, the paper covers the various applications of machine vision in different fields and the promising future for further advancements in computer vision technologies. Received: 19 August 2025 | Revised: 7 January 2026 | Accepted: 28 January 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data sharing is not applicable to this article as no new data were created or analyzed in this study. Author Contribution Statement Mohammad Mehedi Hassan: Conceptualization, Methodology, Software, Formal analysis, Resources, Data curation, Writing – original draft, Visualization, Project administration. Stephen Karungaru: Validation, Investigation, Resources, Writing – review & editing, Supervision, Project administration. Rezaul Bashar: Validation, Writing – review & editing.