Image representations on a budget: Traffic scene classification in a restricted bandwidth scenario

Modern fleet management systems typically monitor the status of hundreds of vehicles by relying on GPS and other simple sensors. Such systems experience significant problems in cases of GPS glitches as well as in areas without GPS coverage. Additionally, when the tracked vehicle is stationary, they cannot discriminate between traffic jams, service stations, parking lots, serious accidents and other interesting scenarios. We propose to alleviate these problems by augmenting the GPS information with a short descriptor of an image captured by an on-board camera. The descriptor allows the server to recognize various scene types by image classification and to subsequently implement suitable business policies. Due to restricted bandwidth we focus on finding a compact image representation that would still allow reliable classification. We therefore consider several state-of-the-art descriptors under tight representation budgets of 512, 256, 128 and 64 components, and evaluate classification performance on a novel image dataset specifically crafted for fleet management applications. Experimental results indicate fair performance even with very short descriptor sizes and encourage further research in the field.