Improving SmallScale Pedestrian Detection Using Informed Context Finding small objects is fundamentally challenging because there is little signal on the object to exploit. For the smallscale pedestrian detection, one must use image evidence beyond the pedestrian extent, which is often formulated as context. Unlike existing object detection methods that use adjacent regions or whole image as the context simply, we focus on more informed contexts exploiting and utilizing to improve smallscale pedestrian detection: firstly, one relationship network is developed to utilize the correlation among pedestrian instances in one image; secondly, two spatial regions, overhead area and feet bottom area, are taken as spatial context to exploit the relevance between pedestrian and scenes; at last, GRU 7 (Gated Recurrent Units) modules are introduced to take encoded contexts as input to guide the feature selection and fusion of each proposal. Instead of getting all of the outputs at once, we also iterate twice to refine the detection incrementally. Comprehensive experiments on Caltech Pedestrian 8 and SJTUSPID 9 datasets, indicate that, with more informed context, the detection performance can be improved significantly, especially for the smallscale pedestrians.

Partguided Network for Pedestrian Attribute Recognition Pedestrian attribute recognition, which can benefit other tasks such as person reidentification and pedestrian retrieval, is very important in video surveillance related tasks. In this paper, we observe that the existing methods tackle this problem from the perspective of multilabel classification without considering the spatial location constraints, which means that the attributes tend to be recognized at certain body parts. Based on that, we propose a novel Partguided Network (PNet), which guides the refined convolutional feature maps to capture different location information for the attributes related to different body parts. The partguided attention module employs the pixlevel classification to produce attention maps which can be interpreted as the probability of each pixel belonging to the 6 predefined body parts. Experimental results demonstrate that the proposed network gives superior performances compared to the stateoftheart techniques.