Toward reliable fusion object detection based on dilated pyramid and semantic attention

Attention-based Object Detection

ESPNet: Efficient Spatial Pyramid of Dilated Convolutions for Semantic Segmentation

We introduce a fast and efficient convolutional neural network, ESPNet, for semantic segmentation of high resolution images under resource constraints. ESPNet is based on a new convolutional module, efficient spatial pyramid (ESP), which is efficient in terms of computation, memory, and power. ESPNet is 22 times faster (on a standard GPU) and 180 times smaller than the stateof-the-art semantic ...

Application of Combined Local Object Based Features and Cluster Fusion for the Behaviors Recognition and Detection of Abnormal Behaviors

In this paper, we propose a novel framework for behaviors recognition and detection of certain types of abnormal behaviors, capable of achieving high detection rates on a variety of real-life scenes. The new proposed approach here is a combination of the location based methods and the object based ones. First, a novel approach is formulated to use optical flow and binary motion video as the loc...

Template Matching based Object Detection Using HOG Feature Pyramid

The Object detection based on histogram of oriented gradients was introduced in the context of detecting humans in images [1]. Detecting human subject in the image is a difficult task due to the fact that they appear in different appearance and poses. Thus the need to come up with a feature that allows human dorm to be detected cleanly even in cluttered background under different illumination. ...

A Gabor Wavelet Pyramid-Based Object Detection Algorithm

We introduce visual object detection architecture, making full use of technical merits of so-called multi-scale feature correspondence in the neurally inspired Gabor pyramid. The remarkable property of the multi-scale Gabor feature correspondence is found with scale-space approaches, which an original image Gabor-filtered with the individual frequency levels is approximated to the corresponding...