Deep Attention Models for Human Tracking Using RGBD

Abstract

Visual tracking performance has long been limited by the lack of better appearance models. These models fail either where they tend to change rapidly, like in motion-based tracking, or where accurate information of the object may not be available, like in color camouflage (where background and foreground colors are similar). This paper proposes a robust, adaptive appearance model which works accurately in situations of color camouflage, even in the presence of complex natural objects. The proposed model includes depth as an additional feature in a hierarchical modular neural framework for online object tracking. The model adapts to the confusing appearance by identifying the stable property of depth between the target and the surrounding object(s). The depth complements the existing RGB features in scenarios when RGB features fail to adapt, hence becoming unstable over a long duration of time. The parameters of the model are learned efficiently in the Deep network, which consists of three modules: (1) The spatial attention layer, which discards the majority of the background by selecting a region containing the object of interest; (2) the appearance attention layer, which extracts appearance and spatial information about the tracked object; and (3) the state estimation layer, which enables the framework to predict future object appearance and location. Three different models were trained and tested to analyze the effect of depth along with RGB information. Also, a model is proposed to utilize only depth as a standalone input for tracking purposes. The proposed models were also evaluated in real-time using KinectV2 and showed very promising results. The results of our proposed network structures and their comparison with the state-of-the-art RGB tracking model demonstrate that adding depth significantly improves the accuracy of tracking in a more challenging environment (i.e., cluttered and camouflaged environments). Furthermore, the results of depth-based models showed that depth data can provide enough information for accurate tracking, even without RGB information.

Critical Overview of Visual Tracking with Kernel Correlation Filter

“…The authors intend to use Kinect RGB-D cameras for this feature extraction. The authors also realize that deep learning methods have proved beneficial for the current research community [43][44][45][46] and intend to explore the potential integration of correlation filters with deep-learning techniques to make the best of both methods.…”

A method of pedestrian flow monitoring based on received signal strength

“…With the rapid development of image processing and machine learning, pedestrian flow monitoring based on machine vision has become a hot research branch. A large number of research results have sprung up in recent years, most of them mainly using individual characteristics such as shape, color, outline, or population characteristics to achieve pedestrian flow monitoring through a combination of SVMs, BP neural networks, CNNs [17,18], and other machine learning algorithms [19][20][21]. Although domestic and foreign experts and scholars have put forward many ingenious methods for the monitoring of pedestrian flow, there are still some problems and deficiencies.…”

A Method of Pedestrian Flow Monitoring Based on Received Signal Strength

“…With the rapid development of image processing and machine learning, pedestrian flow monitoring based on machine vision has become a hot research branch. A large number of research results have sprung up in recent years, most of them mainly using individual characteristics such as shape, color, outline, or population characteristics to achieve pedestrian flow monitoring through a combination of SVMs, BP neural networks, CNNs [9,10], and other machine learning algorithms [11,12,13]. Although domestic and foreign experts and scholars have put forward many ingenious methods for the monitoring of pedestrian flow, there are still some problems and deficiencies.…”