Can you provide an example of a computer vision project you've worked on and the challenges you faced?

One computer vision project I worked on was developing an automated surveillance system for a retail store. The main challenge was to accurately detect and track suspicious activities in real-time. We used OpenCV and TensorFlow for object detection and tracking. I faced challenges in handling occlusions and tracking objects across different camera views. To address this, we implemented a multi-camera tracking system that combined the information from multiple cameras to improve object tracking. Overall, the project was successful and the system was able to detect and track suspicious activities with high accuracy.

One of the computer vision projects I worked on was developing an automated surveillance system for a retail store. The main challenge was to accurately detect and track suspicious activities in real-time while minimizing false positives. To tackle this challenge, I implemented a deep learning-based object detection model using TensorFlow and trained it on a large annotated dataset. I also utilized OpenCV for real-time video processing and GPU acceleration for faster inference. To handle occlusions and tracking across camera views, I developed a multi-camera tracking system that integrated the outputs of individual detection models. The system achieved a high level of accuracy and significantly reduced false positives. Throughout the project, I collaborated closely with the cross-functional team, providing regular updates and incorporating feedback. The success of the system led to its deployment in multiple stores, resulting in a significant decrease in theft incidents.

One of the most challenging computer vision projects I worked on was developing a real-time object recognition system for a self-driving car. The goal was to accurately identify and classify objects on the road, such as cars, pedestrians, and traffic signs, in order to enable safe and reliable autonomous driving. The main challenge was dealing with various real-world scenarios, including different lighting conditions, occlusions, and varying object sizes and orientations. To overcome these challenges, I combined state-of-the-art deep learning models, such as YOLO and SSD, with advanced image processing techniques. I leveraged the power of GPU computing to accelerate both training and inference processes, resulting in real-time object detection. The system achieved high accuracy and robustness, even in challenging conditions. Throughout the project, I collaborated closely with the software engineering team to integrate the object recognition system into the overall self-driving car software stack. I also worked closely with the testing team to ensure the system's reliability and safety. The success of the project led to a significant improvement in the self-driving car's perception capabilities and brought us closer to achieving fully autonomous driving.