Tell me about a time when you had to solve a complex problem related to computer vision. How did you approach it?

One complex problem I encountered in computer vision was developing an object detection system for a self-driving car. The challenge was to accurately identify and track objects in real-time using the car's cameras. To approach this problem, I first conducted thorough research on existing object detection algorithms and frameworks such as YOLO and SSD. Then, I implemented a pipeline using Python and TensorFlow to train a custom object detection model on a large dataset. I fine-tuned the model by experimenting with different hyperparameters and augmenting the dataset. Finally, I integrated the model into the car's software system and conducted extensive testing to ensure its accuracy and efficiency.

One complex problem I faced in computer vision was developing an accurate facial recognition system for a security application. The challenge was to achieve high accuracy even in challenging lighting conditions and with partial occlusions. To tackle this problem, I started by researching state-of-the-art deep learning models and face detection algorithms. I implemented a pipeline using Python and OpenCV, combining face detection with a deep learning-based face recognition model. I trained the model on a diverse dataset of faces from various angles, lighting conditions, and occlusions. To optimize performance, I leveraged GPU computing and parallelization techniques. Additionally, I fine-tuned the model by incorporating techniques such as data augmentation and transfer learning. The resulting system achieved impressive accuracy, even in challenging scenarios.

One complex problem I encountered in computer vision was developing a system for real-time object tracking in surveillance videos. The challenge was to track objects accurately even in crowded scenes with occlusions and frequent appearance changes. To address this problem, I researched and experimented with various object tracking algorithms such as SORT and DeepSORT. I implemented a pipeline using Python and TensorFlow, which involved extracting features from object bounding boxes, predicting object movement, and matching objects across frames. To enhance performance, I utilized GPU computing and parallel processing. The system achieved remarkable accuracy and robustness, even in challenging scenarios, thanks to the combined power of deep learning and traditional tracking techniques. Additionally, I optimized the system by fine-tuning hyperparameters and applying online learning to adapt to appearance changes. Overall, this project showcased my problem-solving skills, proficiency in Python and TensorFlow, and extensive knowledge of computer vision algorithms.