Describe a challenging computer vision problem you tackled and the solution you implemented.

One challenging computer vision problem I tackled was developing an automated gesture recognition system for a robotics project. The goal was to enable a robot to understand and respond to human gestures. The main challenge was to accurately detect and classify a wide range of hand gestures in real-time. To solve this, I trained a deep learning model using a large dataset of annotated hand gesture images. I used Python and TensorFlow for model development and utilized OpenCV for real-time image processing. The solution involved developing custom algorithms for hand detection, tracking, and gesture recognition. The system achieved an accuracy of over 90% in real-world scenarios, enabling the robot to effectively interpret and respond to human gestures.

One challenging computer vision problem I tackled was developing an automated gesture recognition system for a robotics project. The goal was to enable a robot to understand and respond to human gestures in real-time. The main challenge was to accurately detect and classify a wide range of hand gestures quickly and reliably. To solve this, I trained a deep learning model using a large dataset of annotated hand gesture images. I leveraged Python and TensorFlow to develop the model and utilized OpenCV for real-time image processing. I implemented custom algorithms for hand detection, tracking, and gesture recognition, optimizing the performance for real-time execution on a GPU. Through iterative experimentation and fine-tuning, I achieved a high accuracy rate of over 90% in real-world scenarios. This enabled the robot to effectively interpret and respond to human gestures with minimal latency. The automated gesture recognition system significantly enhanced the user interaction experience and improved the overall functionality of the robotic system.

One challenging computer vision problem I tackled was developing an automated gesture recognition system for a humanoid robot used in an interactive museum exhibit. The goal was to enable the robot to understand and respond to a wide range of hand gestures made by visitors. The main challenge was to achieve robustness and accuracy in real-world conditions with varying lighting, backgrounds, and hand positions. To address this, I collected a diverse dataset of annotated hand gesture images from museum visitors, covering different demographics and hand shapes. I applied data augmentation techniques to increase the dataset size and utilized transfer learning with a pre-trained convolutional neural network (CNN) architecture to speed up training. I used Python and TensorFlow to develop the gesture recognition model and integrated it with the robot's perception system. The model incorporated a fully connected network and softmax activation to perform multi-class classification of gestures. To optimize performance, I implemented a multi-threaded pipeline that enabled parallel image processing and inference on a GPU. Through extensive testing and validation, I achieved an accuracy of over 95% in real-world scenarios. The automated gesture recognition system enhanced the visitor experience, allowing them to interact with the robot using intuitive hand gestures. It also showcased the capabilities of computer vision and machine learning in a public setting, generating positive feedback and engagement from museum visitors.