Inspired by the recent work of [@MnihEtAl:nips14] for digit recognition, we consider a recurrent neural network (RNN) based model, where at each time step $t$, both spatial and temporal contextual information are incorporated to decide current transformation action.

Fig. \[fig\_5\] displays the sequential process of applying our active detector model to a possible pedestrian instance $\vec{x}$ of an input image. At start time $t:=1$, let $\vec{b}_{t-1}$ be the initial bbox proposal and $\phi_t \left(\vec{x}\right)$ be the feature representation of its corresponding layer. A finite sequence of bbox transformation actions $\left(a_1, a_2, \ldots \right)$ is then executed based on a recurrent neural network which is trained by reinforcement learning. Note the size of the feature vector $\phi_t \left(\vec{x}\right)$ may vary due to multi-layer representation. Specifically, the C3 to C5 layers of ResNet are employed in this paper, which has different number of feature maps of size 256, 512, and 1024, respectively. Moreover, a spatial pixel location in C3 to C5 layers roughly corresponds to a patch size of 32$\times$32, 64$\times$64, and 128$\times$128 in the original image, respectively. So a tiny pedestrian might simply be overlooked. To address this issue, we introduce a lower bound size of 4$\times$4 over all these three 