Can you give an example of a problem you encountered in a computer vision project and how you solved it?

In a computer vision project I worked on, we encountered a problem with detecting small objects in images. The existing algorithm was not able to accurately detect and classify these objects. To solve this, we gathered a larger dataset of annotated images specifically focusing on small objects. We then fine-tuned the algorithm using transfer learning with a pre-trained model. Additionally, we applied some pre-processing techniques like image resizing and data augmentation to improve the detection accuracy. After these modifications, the algorithm showed significant improvement in detecting small objects with high precision and recall.

One problem I faced in a computer vision project involved accurately detecting pedestrians in crowded scenes. The initial algorithm had low accuracy due to the difficulty in distinguishing pedestrians from cluttered background. To overcome this, I implemented a two-stage object detector using a combination of Haar features and cascaded support vector machines. This approach effectively captured pedestrian features and achieved improved detection results. Additionally, I utilized GPU computing to accelerate the computation process, resulting in real-time performance. To validate the algorithm, I conducted extensive testing on various datasets, achieving high precision and recall. Overall, this solution showcased my proficiency in Python programming, GPU computing, and understanding of computer vision concepts and algorithms.

During a computer vision project, I encountered the challenge of detecting and tracking multiple moving objects in real-time from a video stream. The existing algorithm struggled to handle occlusions and frequent changes in object appearances. To address this, I implemented a multi-object tracking algorithm based on the Hungarian algorithm and Kalman filtering. This approach enabled accurate object tracking by efficiently handling occlusions and predicting object trajectories. To improve the robustness of the algorithm, I incorporated a deep learning-based object detection model trained on a large annotated dataset. This integration allowed for precise object localization even in complex scenarios. Additionally, I optimized the algorithm for GPU execution, leveraging parallel processing to achieve real-time performance. The solution demonstrated my expertise in machine learning frameworks like TensorFlow and my comprehensive understanding of computer vision concepts and applications.