In this study, a discriminative detector for object context is designed and tested. The context-feature is simple to implement, feed-forward, and effective across multiple object types in a street-scenes environment.
Using context alone, we demonstrate robust detection of locations likely to contain bicycles, cars, and pedestrians. Furthermore, experiments are conducted so as to address several open questions regarding visual context. Specifically, it is demonstrated that context may be determined from low level visual features (simple color and texture descriptors) sampled over a wide receptive field. At least for the framework tested, high level semantic knowledge, e.g, the nature of the surrounding objects, is superfluous. Finally, it is shown that when the target object is unambiguously visible, context is only marginally useful.