Correlation-based and content-enhanced network for video style transfer

Pattern Analysis and Applications - Artistic style transfer aims to migrate the style pattern from a referenced style image to a given content image, which has achieved significant advances in...     

Deep feature learning for person re-identification in a large-scale crowdsourced environment

Finding the same individual across cameras in disjoint views at different locations and times, which is known as person re-identification (re-id), is an important but difficult task in intelligent visual surveillance. However, to build a practical re-id system for large-scale and crowdsourced environments, the existing approaches are largely unsuitable because of their high model complexity. In this paper, we present a deep feature learning framework for automated large-scale person re-id with low computational cost and memory usage. The experimental results show that the proposed framework is comparable to the state-of-the-art methods while having low model complexity.     

Person tracking and reidentification: Introducing Panoramic Appearance Map (PAM) for feature representation

This paper develops a concept of Panoramic Appearance Map (PAM) for performing person reidentification in a multi-camera setup. Each person is tracked in multiple cameras and the position on the floor plan is determined using triangulation. Using the geometry of the cameras and the person location, a panoramic map centered at the person’s location is created with the horizontal axis representing the azimuth angle and vertical axis representing the height. Each pixel in the map image gets color information from the cameras which can observe it. The maps between different tracks are compared using a distance measure based on weighted SSD in order to select the best match. Temporalintegration by registering multiple maps over the tracking period improves the matching performance. Experimental results of matching persons between two camera sets show the effectiveness of the approach. This work has been sponsored by the Technical Support Working Group (TSWG) of US Department of Defence (DoD).     

Image generation and constrained two-stage feature fusion for person re-identification

Generative adversarial network is widely used in person re-identification to expand data by generating auxiliary data. However, researchers all believe that using too much generated data in the training phase will reduce the accuracy of re-identification models. In this study, an improved generator and a constrained two-stage fusion network are proposed. A novel gesture discriminator embedded into the generator is used to calculate the completeness of skeleton pose images. The improved generator can make generated images more realistic, which would be conducive to feature extraction. The role of the constrained two-stage fusion network is to extract and utilize the real information of the generated images for person re-identification. Unlike previous studies, the fusion of shallow features is considered in this work. In detail, the proposed network has two branches based on the structure of ResNet50. One branch is for the fusion of images that are generated by the generated adversarial network, the other is applied to fuse the result of the first fusion and the original image. Experimental results show that our method outperforms most existing similar methods on Market-1501 and DukeMTMC-reID.

     

A feature extraction method for person re-identification based on a two-branch CNN

Multimedia Tools and Applications - A two-branch convolutional neural network (CNN) architecture for feature extraction in person re-identification (re-ID) based on video surveillance is proposed....     

Automatic resource allocation in a distributed camera network

In this paper, we present a hierarchical smart resource coordination and reconfiguration framework for distributed systems. We view the coordination problem as one of context aware resource reconfiguration. The fundamental unit in this hierarchy is a Fault Containment Unit (FCU) that provides run-time fault-tolerance by deciding on the best alternative course of action when a failure occurs. FCUs are composed hierarchically and are responsible for dynamically reconfiguring failing FCUs at lower levels. When such a reconfiguration is not possible, FCUs propagate the failure upward for resolution. We evaluate the effectiveness of our framework in a people tracking application using a network of cameras. The task for our multi-camera network is to allocate pairs of cameras that localize a subject optimally given the current run-time context. The system automatically derives policies for switching between camera pairs that enable robust tracking while being attentive to certain performance measures. Our approach is unique in that we model the dynamics in the scene and the camera network configuration steers the policies to provide robust tracking.     

Efficient fuzzy feature matching and optimal feature points for multiple objects tracking in fixed and active camera models

Multimedia Tools and Applications - In computer vision, the multiple objects tracking play a vital challenging role. To solve the issues in this research field, various traditional techniques had...     

Learning generalizable deep feature using triplet-batch-center loss for person re-identification

Dear editor, Metric learning loss functions are important for deep learning-based person re-identification(re-ID).Several loss functions,such as triplet loss,ce...     

A multi-branch attention and alignment network for person re-identification

Applied Intelligence - Person re-identification plays a critical role in video surveillance and has a variety of applications. However, the body misalignment caused by detectors or pose changes...     

Tracking a person with pre-recorded image database and a pan, tilt, and zoom camera

Abstract. This paper proposes a novel tracking strategy that can robustly track a person or other object within a fixed environment using a pan, tilt, and zoom camera with the help of a pre-recorded image database. We define a set of camera states which is sufficient to survey the environment for the target. Background images for these camera states are stored as an image database. During tracking, camera movements are restricted to these states. Tracking and segmentation are simplified, as each tracking image can be compared with the corresponding pre-recorded background image. Received: 26 August 1999 / Accepted: 22 February 2000     

Automatic feature point extraction and tracking in image sequences for arbitrary camera motion

An automatic egomotion compensation based point correspondence algorithm is presented. A basic problem in autonomous navigation and motion estimation is automatically detecting and tracking features in consecutive frames, a challenging problem when camera motion is significant. In general, feature displacements between consecutive frames can be approximately decomposed into two components: (i) displacements due to camera motion which can be approximately compensated by image rotation, scaling, and translation; (ii) displacements due to object motion and/or perspective projection. In this paper, we introduce a two-step approach: First, the motion of the camera is compensated using a computational vision based image registration algorithm. Then consecutive frames are transformed to the same coordinate system and the feature correspondence problem is solved as though tracking moving objects for a stationary camera. Methods of subpixel accuracy feature matching, tracking and error analysis are introduced. The approach results in a robust and efficient algorithm. Results on several real image sequences are presented.The support of the Advanced Research Projects Agency (ARPA Order No. 8459) and the U.S. Army Engineer Topographic Laboratories under Contract DACA 76-92-C-0009 is gratefully acknowledged.     

Autoconfiguration of a dynamic nonoverlapping camera network.

In order to monitor sufficiently large areas of interest for surveillance or any event detection, we need to look beyond stationary cameras and employ an automatically configurable network of nonoverlapping cameras. These cameras need not have an overlapping field of view and should be allowed to move freely in space. Moreover, features like zooming in/out, readily available in security cameras these days, should be exploited in order to focus on any particular area of interest if needed. In this paper, a practical framework is proposed to self-calibrate dynamically moving and zooming cameras and determine their absolute and relative orientations, assuming that their relative position is known. A global linear solution is presented for self-calibrating each zooming/focusing camera in the network. After self-calibration, it is shown that only one automatically computed vanishing point and a line lying on any plane orthogonal to the vertical direction is sufficient to infer the dynamic network configuration. Our method generalizes previous work which considers restricted camera motions. Using minimal assumptions, we are able to successfully demonstrate promising results on synthetic, as well as on real data.     

A Siamese inception architecture network for person re-identification

Person re-identification is an extremely challenging problem as person’s appearance often undergoes dramatic changes due to the large variations of viewpoints, illuminations, poses, image resolutions, and cluttered backgrounds. How to extract discriminative features is one of the most critical ways to address these challenges. In this paper, we mainly focus on learning high-level features and combine the low-level, mid-level, and high-level features together to re-identify a person across different cameras. Firstly, we design a Siamese inception architecture network to automatically learn effective semantic features for person re-identification in different camera views. Furthermore, we combine multi-level features in null space with the null Foley–Sammon transform metric learning approach. In this null space, images of the same person are projected to a single point, which minimizes the intra-class scatter to the extreme and maximizes the relative inter-class separation simultaneously. Finally, comprehensive evaluations demonstrate that our approach achieves better performance on four person re-identification benchmark datasets, including Market-1501, CUHK03, PRID2011, and VIPeR.     

Prowling autonomous mobile robot with a network camera

This research aimed to develop an autonomous mobile robot that helps various kinds of people. The evasion of obstacles is absolutely imperative so that the robot can act in a human-life environment. Therefore, we developed a robot that moves through doors and avoids obstacles with the help of images taken by a camera set on the robot. This work was presented in part at the 13th International Symposium on Artifical Life and Robotics, Oita, Japan, January 31–February 2, 2008     

Optimal feature points for tracking multiple moving objects in active camera model

Object tracking is an important task in computer vision that is essential for higher level vision applications such as surveillance systems, human-computer interaction, industrial control, smart compression of video, and robotics. Tracking, however, cannot be easily accomplished due to challenges such as real-time processing, occlusions, changes in intensity, abrupt motions, variety of objects, and mobile platforms. In this paper, we propose a new method to estimate and eliminate the camera motion in mobile platforms, and accordingly, we propose a set of optimal feature points for accurate tracking. Experimental results on different videos show that the proposed method estimates camera motion very well and eliminate its effect on tracking moving objects. And the use of optimal feature points results in a promising tracking. The proposed method in terms of accuracy and processing time has desirable results compared to the state-of-the-art methods.     

Learning discriminative features with a dual-constrained guided network for video-based person re-identification

Multimedia Tools and Applications - Video-based person re-identification (ReID) aims at matching pedestrians in a large video gallery across different cameras. However, some interference factors in...