An intelligent retrievable object-tracking system with real-time edge inference capability

An intelligent retrievable object-tracking system assists users in quickly and accurately locating lost objects. However, challenges such as real-time processing on edge devices, low image resolution, and small-object detection significantly impact the accuracy and efficiency of video-stream-based systems, especially in indoor home environments. To overcome these limitations, a novel real-time intelligent retrievable object-tracking system is designed. The system incorporates a retrievable object-tracking algorithm that combines DeepSORT and sliding window techniques to enhance tracking capabilities. Additionally, the YOLOv7-small-scale model is proposed for small-object detection, integrating a specialized detection layer and the convolutional batch normalization LeakyReLU spatial-depth convolution module to enhance feature capture for small objects. TensorRT and INT8 quantization are used for inference acceleration on edge devices, doubling the frames per second. Experiments on a Jetson Nano (4 GB) using YOLOv7-small-scale show an 8.9% improvement in recognition accuracy over YOLOv7-tiny in video stream processing. This advancement significantly boosts the system's performance in efficiently and accurately locating lost objects in indoor home settings.