Tag: T.Burghardt

Lili Tao, Tilo Burghardt, Sion Hannuna, Massimo Camplani, Adeline Paiement, Dima Damen, Majid Mirmehdi, Ian Craddock. A Comparative Home Activity Monitoring Study using Visual and Inertial Sensors, 17th International Conference on E-Health Networking, Application and Services (IEEE HealthCom), 2015

Monitoring actions at home can provide essential information for rehabilitation management. This paper presents a comparative study and a dataset for the fully automated, sample-accurate recognition of common home actions in the living room environment using commercial-grade, inexpensive inertial and visual sensors. We investigate the practical home-use of body-worn mobile phone inertial sensors together with an Asus Xmotion RGB-Depth camera to achieve monitoring of daily living scenarios. To test this setup against realistic data, we introduce the challenging SPHERE-H130 action dataset containing 130 sequences of 13 household actions recorded in a home environment. We report automatic recognition results at maximal temporal resolution, which indicate that a vision-based approach outperforms accelerometer provided by two phone-based inertial sensors by an average of 14.85% accuracy for home actions. Further, we report improved accuracy of a vision-based approach over accelerometry on particularly challenging actions as well as when generalising across subjects.

Data collection and processing

For visual data, we simultaneously collect RGB and depth images using the commercial product Asus Xmotion. Motion information can be recovered best from RGB data as it contains rich texture and colour information. Depth information, on the other hand, reveals details of the 3D configuration. We extract and encode both motion and depth features over the area of a bounding box as returned by the human detector and tracker provided by the OpenNI SDK[1]. For collecting inertial data, we opt for subjects to wear two accelerometers mounted at the centre of the waist and the dominant wrist.
The figure below gives an overview of the system.

 Figure 1. Experimental setup.

SPHERE-H130 Action Dataset

We introduce the SPHERE-H130 action dataset for human action recognition from RGB-Depth and inertial sensor data captured in a real living environment. The dataset is generated over 10 sessions by 5 subjects containing 13 action categories per session: sit still, stand still, sitting down, standing up, walking, wiping table, dusting, vacuuming, sweeping floor, cleaning stain, picking up, squatting, upper body stretching. Overall, recordings correspond to about 70 minutes of total time captured. Actions were simultaneously captured by the Asus Xmotion RGB-depth camera and the two wireless accelerometers. Colourand depth images were acquired at a rate of 30Hz. The accelerometer data was captured at about 100Hz sampled down to 30Hz, a frequency recognised as optimal for human action recognition.

     sitting                            standing                               sitting down                           standing up                               walking

wiping                              dusting                               vacumming                                  sweeping                       cleaning stain

Picking                          squatting                           stretching

Figure 2. Sample videos from the dataset

Results

Vision can be more accurate than Accelerometers. Figure 3 depicts the recognition confusion matrices corresponding to the use of inertial and visual sensors, respectively.

Figure 3. The confusion matrices by (left) visual data and (right) accelerometer data.

Publication and Dataset

The dataset and the proposed method is presented in the following paper:

• Lili Tao, Tilo Burghardt, Sion Hannuna, Massimo Camplani, Adeline Paiement, Dima Damen, Majid Mirmehdi, Ian Craddock. A Comparative Home Activity Monitoring Study using Visual and Inertial Sensors, 17th International Conference on E-Health Networking, Application and Services (IEEE HealthCom), 2015

SPHERE-H130 action dataset can be downloaded here.

References

[1] OpenNI User Guide, OpenNI organization, November 2010. Available: http://www.openni.org/documentation

Massimo Camplani, Sion Hannuna,  Majid Mirmehdi, Dima Damen, Adeline Paiement, Lili Tao, Tilo Burghardt. Real-time RGB-D Tracking with Depth Scaling Kernelised Correlation Filters and Occlusion Handling. British Machine Vision Conference, September 2015.

S. Hannuna, M. Camplani, J. Hall, M. Mirmehdi, D. Damen, T. Burghardt, A. Paiement, L. Tao, DS-KCF: A real-time tracker for RGB-D data, Journal of Real-Time Image Processing (2016). Open Access Publication can be downloaded here DOI: 10.1007/s11554-016-0654-3

The recent surge in popularity of real-time RGB-D sensors has encouraged research into combining colour and depth data for tracking. The results from a few, recent works in RGB-D tracking have demonstrated that state-of-the-art RGB tracking algorithms can be outperformed by approaches that fuse colour and depth, for example [1, 3, 4, 5]. In this paper, we propose a real-time RGB-D tracker which we refer to as the Depth Scaling Kernelised Correlations Filters (DS-KCF). It is based on, and improves upon, the RGB Kernelised Correlation Filters tracker (KCF) from [2]. In this paper, we propose a real-time RGB-D tracker which we refer to as the Depth Scaling Kernelised Correlations Filters (DS-KCF). It is based on, and improves upon, the RGB Kernelised Correlation Filters tracker (KCF) from [2]. KCF is based on the use of the ‘kernel trick’ to extend correlation filters for very fast RGB tracking. The KCF tracker has important characteristics, in particular its ability to combine high accuracy and processing speed as demonstrated in [2, 6].

The proposed RGB-D single object tracker is able to exploit depth information to handle scale changes, occlusions, and shape changes. Despite the computational demands of the extra functionalities, we still achieve real-time performance rates of 35-43 fps in Matlab, and 187 fps in our C++ implementation. Our proposed method includes fast depth-based target object segmentation that enables, (i) efficient scale change handling within the KCF core functionality in the Fourier domain, (ii) the detection of occlusions by temporal analysis of the target’s depth distribution, and (iii) the estimation of a target’s change of shape through the temporal evolution of its segmented silhouette allows. Both the Matlab and C++ versions of our software are available in the public domain.

DS–KCF

This paper contains an extension of the DS-KCF proposed in [7]. In this section we provide a detailed description of the core modules comprising DS-KCF, which extend the KCF tracker in a number of different ways. We integrate an efficient combination of colour and depth features in the KCF-tracking scheme. We provide a change of scale module, based on depth distribution analysis, that allows to efficiently modify the tracker’s model in the Fourier domain. Different to other works that deal with change of scale within the KCF framework, such as [2,3], our approach estimates the change of scale with minimal impact on real-time performance. We also introduce an occlusion handling module that is able to identify sudden changes in the target region’s depth histogram and to recover lost tracks. Finally, a change of shape module, based on the temporal evolution of the segmented target’s silhouette, is integrated into the framework. To improve the tracking performance during occlusions, we have added a simple Kalman filter motion model.

A detailed overview of the modules of the proposed tracker is shown in the figure below. Initially, depth data in the target region is segmented to extract relevant features for the target’s depth distribution. Then, modelled as a Gaussian distribution, changes in scale guide the update in the target’s model. At the same time, region depth distribution is deployed to enable the detection of possible occlusions. During an occlusion, the model is not updated and the occluding object is tracked to guide the target search space. Kalman filtering is used to predict the position of the target and the occluding object in order to improve the occlusion recovery strategy. Further, segmentation masks are accumulated over time and used to detect significant changes of shape of the object.

 

Block diagram of the proposed DS-KCF tracker

Results on Princeton Dataset [4] (validation set)

We compare tracking performance by reporting the precision value for an error threshold equal to 20 pixels (P20), the area under the curve (AUC) of success plot measure, and the number of processed frames per second (fps). Table 1 shows that the proposed DS-KCF tracker outperforms the baseline KCF leading to better results both in terms of AUC and P20 measures. DS-KCF also outperforms the other two RGB-D trackers tested, Prin-Track [4] and OAPF [3]. Furthermore, the average processing rate in the Prin-Track (RGB-D) is 0.14fps and 0.9fps for the OAPF tracker in striking contrast to 40fps for DS-KCF. Example results of the trackers are shown in the videos below. Results on the test set of the Princeton dataset can be found here

 

 Trackers’ performance on Princeton Evaluation Dataset [4]

Overall, only two approaches, OAPF [5] and RGBDOcc+OF [4], obtain a higher Avg. Rank (by approximately 0.6% more) and a higher average AUC value (by approximately 1.4% more). However, as reported by their authors, these approaches achieve a very low processing rate of less than 1 fps. Our proposed method has an average processing rate ranging from 35 to 43 fps for its Matlab implementation and 187 fps for its C++ version.

Updated results can be found in the dataset webpage

Qualitative examples of the DS-KCF’s performance on Princeton Dataset [4] and BoBoT-D dataset [1] are shown below.

GITHUB DS–KCF code

The C++ and MATLAB version of the code are also available in GITHUB here. Code of the DSKCF tracker may be used on the condition of citing our paper “DS-KCF: A real-time tracker for RGB-D data. Journal of Real-Time Image Processing″ and the SPHERE project. The code is released under BSD license.

DSKCF C++ code (no shape Module)

The C++ code of the new version of the DSKCF tracker may be used on the condition of citing our paper “DS-KCF: A real-time tracker for RGB-D data. Journal of Real-Time Image Processing″ and the SPHERE project. The code is released under BSD license and it can be downloaded here. Please not this does not contain the shape-handling module

DSKCF Matlab code (with shape Module)

The Matlab code of the new version of the DSKCF tracker may be used on the condition of citing our paper “DS-KCF: A real-time tracker for RGB-D data. Journal of Real-Time Image Processing″ and the SPHERE project. The code is released under BSD license and it can be downloaded here

DSKCF Matlab code (BMVC VERSION)

The Matlab code of the DSKCF tracker can be downloaded here. It may be used on the condition of citing our paper “Real-time RGB-D Tracking with Depth Scaling Kernelised Correlation Filters and Occlusion Handling, BMVC2015″ and the SPHERE project. The code is released under BSD license and it can be downloaded here

RotTrack DATASET

The datset can be used for research purposes on the condition of citing our paper “DS-KCF: A real-time tracker for RGB-D data. Journal of Real-Time Image Processing″ and the SPHERE project. The dataset can be downloaded from the University of Bristol official repository data.bris

References

[1] G. García, D. Klein, J. Stückler, S. Frintrop, and A. Cremers. Adaptive multi-cue 3D tracking of arbitrary objects. In Pattern Recognition, pages 357–366. 2012.
[2] J. F. Henriques, R. Caseiro, P. Martins, and J. Batista. High-speed tracking with kernelized correlation filters. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 2015.
[3] T. Meshgi, S. Maeda, S. Oba, H. Skibbe, Y. Li, and S. Ishii. Occlusion aware particle filter tracker to handle complex and persistent occlusions. Computer Vision and Image Understanding, 2015 to appear.
[4] S. Song and J. Xiao. Tracking revisited using RGBD camera: Unified benchmark and baselines. In Computer Vision (ICCV), 2013 IEEE International Conference on, pages 233–240, 2013.
[5] Q. Wang, J. Fang, and Y. Yuan. Multi-cue based tracking. Neurocomputing, 131(0):227 – 236, 2014.
[6] Y. Wu, J. Lim, and M. Yang. Online Object Tracking: A Benchmark. In Computer Vision and Pattern Recognition (CVPR), 2013 IEEE Conference on, pages 2411–2418, 2013.
[7] Massimo Camplani, Sion Hannuna, Majid Mirmehdi, Dima Damen, Adeline Paiement, Lili Tao, Tilo Burghardt. Real-time RGB-D Tracking with Depth Scaling Kernelised Correlation Filters and Occlusion Handling.
[8] Danelljan M, Haager G, Shahbaz Khan F, Felsberg M (2014) Accurate Scale Estimation for Robust Visual Tracking. In: BMVC 2014
[9] Li Y, Zhu J (2015) A scale adaptive kernel correlation Filter tracker with feature integration. In: ECCV Workshops, vol 8926, pp 254-265.

Sphere Project main objective is to develop a multimodality sensing platform, based on low-cost devices: ranging from on-body sensors, environmental sensors and video based sensors. The SPHERE platform is aiming at efficiently tackling the problem of healthcare monitoring at home. Its vision is not to develop fundamentally-new sensors for individual health conditions but rather to impact all these healthcare needs simultaneously through data-fusion and pattern-recognition from a common platform of non-medical/environmental sensors at home. The system will be general-purpose, low-cost and hence scalable. Sensors will be entirely passive, requiring no action by the user and hence suitable for all patients including the most vulnerable. A central hypothesis is that deviations from a user’s established pattern of behaviour in their own home have particular, unexploited, diagnostic value.

Computer Vision in Sphere: WP2 (Vision Team)

The main objectives of WP2 consist of developing an efficient, real-time multi-camera system for activity monitoring in the home environment. The system will be based on low cost cameras and depth sensors to estimate client’s position and to analyse their movements to extract features for use for action understanding and activity recognition.

The vision team are developing a video based action recognition and multi-user tracking system for the house environment. This solution will allow the system to estimate the activity/inactivity level of the user during their daily life. The platform has been tested in SPHERE’s house and integrated with the other sensor systems; providing a unique multisensory system for data collection. On-going video work includes a collaboration with respiratory physicians in Bristol developing and validating video-based systems for monitoring breathing.

Related Projects

Online quality assessment of human movements from skeleton data
The objective of this project is to evaluate the quality of human movements from visual information. This has use in a broad range of applications, such as diagnosis and rehabilitation.

Real Time RGB-D tracker: DS-KCF 
The objective of this project is develop a real time RGB-D tracker based on Kernelised Correlation Filters

WP2 Publications

2015

• Massimo Camplani, Sion Hannuna,  Majid Mirmehdi, Dima Damen, Adeline Paiement, Lili Tao, Tilo Burghardt. Real-time RGB-D Tracking with Depth Scaling Kernelised Correlation Filters and Occlusion Handling. British Machine Vision Conference, September 2015.
• N. Zhu, T. Diethe, M. Camplani, L. Tao, A. Burrows, N. Twomey, D. Kaleshi, M. Mirmehdi, P. Flach, I. Craddock, Bridging eHealth and the Internet of Things: The SPHERE Project, IEEE Intelligent Systems, (to appear).
• A Multi-modal Sensor Infrastructure for Healthcare in a Residential Environment. P. Woznowski, X. Fafoutis, T. Song, S. Hannuna, M. Camplani, L. Tao, A. Paiement, E. Mellios, M. Haghighi, N. Zhu, G. Hilton, D. Damen, T. Burghardt, M. Mirmehdi, R. Piechocki, D. Kaleshi and I. Craddock. IEEE International Conference on Communications (ICC), Workshop on ~ICT-enabled services and technologies for eHealth and Ambient Assisted Living.

2014

• A. Paiment, L. Tao, S. Hannuna, M. Camplani, D. Damen and M. Mirmehdi, Majid (2014). Online quality assessment of human movement from skeleton data. British Machine Vision Conference (BMVC), Nottingham, UK