Here are three promising computer vision project ideas, each addressing distinct applications and technical challenges:
1. Real-Time Object Detection and Tracking for Surveillance or Sports Analysis A practical project involves building a system that detects and tracks objects in real-time video streams. For example, you could create a traffic monitoring tool that identifies vehicles, bicycles, and pedestrians, then tracks their movements to analyze congestion patterns. Using frameworks like YOLO (You Only Look Once) or EfficientDet for object detection, combined with tracking algorithms like SORT (Simple Online and Realtime Tracking), you can achieve efficient performance. Edge deployment on devices like the NVIDIA Jetson Nano or Raspberry Pi with Coral TPU accelerators allows for low-latency processing. Developers can experiment with optimizing model size using TensorFlow Lite or ONNX Runtime for resource-constrained environments. A sports analytics extension might track player movements in a basketball game, providing insights into team strategies or player performance metrics.
2. Medical Image Segmentation for Disease Diagnosis Medical imaging projects offer tangible societal impact. A segmentation model that identifies tumors in MRI or CT scans could assist radiologists in diagnosing conditions like brain cancer. The U-Net architecture is a strong starting point due to its effectiveness in handling high-resolution medical images. Public datasets like the BraTS Challenge (Brain Tumor Segmentation) provide annotated data for training. Another angle is detecting diabetic retinopathy in retinal scans using convolutional neural networks (CNNs), which could automate early screening in clinics. Tools like PyTorch Lightning and MONAI (Medical Open Network for AI) simplify data preprocessing and model training. To address data scarcity, techniques like data augmentation with synthetic samples (using GANs) or transfer learning from pre-trained models like ResNet-50 can improve accuracy. Integrating such models into open-source platforms like 3D Slicer would make them accessible to medical professionals.
3. Augmented Reality (AR) Applications with Pose Estimation Developing AR applications that overlay digital content onto real-world scenes requires robust pose estimation. A project could involve creating an interactive museum guide where users point their smartphone cameras at exhibits to trigger informational animations. MediaPipe’s BlazePose or OpenCV’s ARUCO markers can handle real-time pose detection and tracking. For more advanced scenarios, implement SLAM (Simultaneous Localization and Mapping) to map environments and anchor virtual objects consistently. Another idea is enhancing video conferencing with background replacement or gesture-based controls using segmentation models like DeepLabv3+. Using Unity or Unreal Engine with ARCore/ARKit plugins, developers can prototype cross-platform AR experiences. Challenges include optimizing inference speed for mobile GPUs and reducing drift in pose estimation—issues that invite experimentation with model quantization or multi-threaded processing pipelines.
Each project balances technical depth with real-world applicability, providing opportunities to explore model optimization, domain-specific challenges, and deployment strategies.