A Fast Video Image Detection using TensorFlow Mobile Networks for Racing Cars
With the growth of the Internet of Things, we see an increase in the importance of analysis of data from the edge, often with the results needed in real-time.
Indy Car series is one of the well-known racing series in North America. All cars are equipped with multiple cameras. The video streams captured by these cameras can be used for detection and predictive tasks to increase race safety and develop better strategies to win the race. Moreover, the data can be used together with the telemetry data to provide better analysis and predictions for the drivers and the teams. In a lot of video analytics tasks, the tasks begin with object detection as its foundation. The existing pretrained object detection models are inadequate to detect IndyCar race cars.
Therefore, we have created a new dataset and have compared three different Single Shot Multibox Detector models from TensorFlow Detection Model Zoo. We run experiments on CPU and GPU. Since transferring the data from edge devices to a server, running inference, and sending the result back is time and resource consuming, we also test mobile detection models on an Edge TPU, which is a Google Coral Dev Board. Our initial results show that the Edge TPU gives the best inference time, and it is more suitable for a real-time machine learning task.