  Vision-based object detection is one of the fundamental functions in numerous
traffic scene applications such as self-driving vehicle systems and advance
driver assistance systems (ADAS). However, it is also a challenging task due to
the diversity of traffic scene and the storage, power and computing source
limitations of the platforms for traffic scene applications. This paper
presents a generalized Haar filter based deep network which is suitable for the
object detection tasks in traffic scene. In this approach, we first decompose a
object detection task into several easier local regression tasks. Then, we
handle the local regression tasks by using several tiny deep networks which
simultaneously output the bounding boxes, categories and confidence scores of
detected objects. To reduce the consumption of storage and computing resources,
the weights of the deep networks are constrained to the form of generalized
Haar filter in training phase. Additionally, we introduce the strategy of
sparse windows generation to improve the efficiency of the algorithm. Finally,
we perform several experiments to validate the performance of our proposed
approach. Experimental results demonstrate that the proposed approach is both
efficient and effective in traffic scene compared with the state-of-the-art.
