@DaiEtAl:nips16] that a larger set of proposals (e.g., 2000) per image has almost no additional benefit, the number of initial proposals is set to 300 in our approach.

Each mini-batch consists of 128 randomly sampled pedestrian proposals from one randomly selected image, which is composed of 32 positive proposals and 96 negative proposals. During training and performance evaluation, a pedestrian bbox prediction is considered to be positive, if its intersection over union with a ground-truth bbox is larger than 0.5, or it has the highest IoU overlap with a ground-truth bbox; It is assigned negative if its IoU ratio is less than 0.3 for any of the ground-truth bboxes. In our active detector module, each RNN state vector $\vec{s}_t \in \mathbb{R}^{64}$ contains 64 LSTM units of history records. Meanwhile the feature vector obtained from the RoI pooling layer of ResNet-50 delineated by previous bbox prediction is $\vec{o}_t \in \mathbb{R}^{1024}$. During reinforcement learning training phase, the learning rate is linearly annealed from its initial value (usually between 0.1 and 0.0001) to 0.

For benchmarks, three widely used datasets are considered in our experiments, namely Caltech [@DolEtAl:cvpr09], ETH [@DolEtAl:bmvc10] and TUD-Brussels [@WojWalSch:cvpr09]. The Caltech pedestrian dataset consists of approximately 10 hours of 