Object detection is a critical field in computer vision that focuses on recognizing and locating various objects within images or video streams. This study utilizes the advanced object detection model You Only Look Once version 7 (YOLOv7) to identify vehicles in a designated parking area. To enhance this capability, we integrate a tracking algorithm known as Deep Simple Online and Realtime Tracking (DeepSORT), which allows for continuous monitoring of parked cars and supports the detection model in maintaining identification for as long as necessary. The YOLOv7 model has been trained with data augmentation techniques, achieving an impressive evaluation score of 0.86 in vehicle detection accuracy. The DeepSORT algorithm is responsible for tracking any cars entering the parking zone, triggering a timer that records the duration of each vehicle's occupancy in the parking space. The system provides a clear display of information by indicating the status of each parking space in the upper right corner of the video feed. This displayed information specifies whether each space is occupied or available. A help function has been incorporated, enabling users to capture coordinates of any pixel within the video to assist in accurately mapping parking spots. Evaluation results reveal that the system effectively detects and tracks parked cars for up to 200 s across two distinct scenarios. Additionally, to assess real-time performance, we provide an Average Frames Per Second (Avg FPS) metric displayed in the upper left corner, with the system recording a remarkable 32.1 FPS. This comprehensive approach combines robust detection and tracking capabilities, resulting in an efficient and user-friendly parking management solution.