In this paper, we propose a novel congested crowd counting network for crowd density estimation, i.e., the Adaptive Multi-scale Context Aggregation Network (MSCANet). MSCANet efficiently leverages the spatial context information to accomplish crowd density estimation in a complicated crowd scene. To achieve this, a multi-scale context learning block, called the Multi-scale Context Aggregation module (MSCA), is proposed to first extract different scale information and then adaptively aggregate it to capture the full scale of the crowd. Employing multiple MSCAs in a cascaded manner, the MSCANet can deeply utilize the spatial context information and modulate preliminary features into more distinguishing and scale-sensitive features, which are finally applied to a 1 × 1 convolution operation to obtain the crowd density results. Extensive experiments on three challenging crowd counting benchmarks showed that our model yielded compelling performance against the other state-of-the-art methods. To thoroughly prove the generality of MSCANet, we extend our method to two relevant tasks: crowd localization and remote sensing object counting. The extension experiment results also confirmed the effectiveness of MSCANet.

In this paper, a novel ZVS PWM interleaved flyback converter topology is presented. Operation principle and steady state of the presented converter are analyzed. Comparing to conventional interleaved flyback converter, two transformers of the proposed converter can be reset with a clamp capacitor C_c and input voltage V_i, respectively. Thus, it can reduce losses which are produced by snubber circuit and increase efficiency. Moreover, all switches can achieve zero-voltage-switching (ZVS) through the resonance which exists between the transformer leakage inductance, parasitic capacitance of switches and clamp capacitor. As a result, it has some advantages such as simple topology, soft-switching, lower output ripple and higher efficiency. Finally, the analyses above are verified by simulation and experimental results.