Scene context is more than a Bayesian prior: Competitive vehicle detection with restricted detectors

Thomas Hecht, Mrinal Mohit, Egor Sattarov, Alexander Gepperth

Abstract: We present an approach for making use of scene or situation context in object detection, aiming for state-of-the-art performance while dramatically reducing computational cost. While existing approaches are inspired by Bayes' rule, training context-independent detectors and combining them with context priors in hindsight, we propose to integrate these context priors into detector design itself, through algorithmic choices and/or pre-selection of training examples. Although such restricted detectors will, as a consequence, be valid only in regions compatible with context priors, the corresponding simplification of the object-vs-background decision problem will lead to reduced computation time and/or increased detection performance. We verify this experimentally by analyzing vehicle detection performance in a realistically simulated inner-city environment where context priors are defined by a road surface mask obtained from the simulation tool. Comparing a restricted detector, based on horizontal edges detection refined by neural network confirmation, to a generic HOG+SVM-based approach which takes into account the road context prior, we show that the restricted detector shows superior vehicle detection performance at a vastly reduced computational cost. We show qualitative results that permit the conclusion that the restricted detector will perform well on real-world scenes if appropriate road context priors are available.