Fast Vehicle Turning-Movement Counting Using Localization-Based Tracking
Despite the high utility of traffic volume and turning movement data, such data is still hard to come by for the vast majority of roadways and intersections in nearly every city. Edge computing devices offer a promising tool for recording turning movement data if lightweight algorithms can be designed to run in real-time with relatively modest computational complexity. To that end, this work presents Vehicle Turning-Movement Counting using Localization-based Tracking (LBT-Count). This method is fast because it never performs detection on a full frame. Instead, only a few portions of the image are cropped and used to detect objects within the frame. The method achieves competitive performance on the public evaluation server for Track 1 of the AI City Challenge (7th overall on the first 50% of data). Furthermore, we show that LBT-Count is 52% faster than an analogous counting algorithm utilizing a traditional tracking-by-detection framework on available challenge data.