Abandoned object detection using multi-layer motion detection

Abandoned object detection (AOD) systems are required to run in high traffic situations, with high levels of occlusion. Systems rely on background segmentation techniques to locate abandoned objects, by detecting areas of motion that have stopped. This is often achieved by using a medium term motion detection routine to detect long term changes in the background. When AOD systems are integrated into person tracking system, this often results in two separate motion detectors being used to handle the different requirements. We propose a motion detection system that is capable of detecting medium term motion as well as regular motion. Multiple layers of medium term (static) motion can be detected and segmented. We demonstrate the performance of this motion detection system and as part of an abandoned object detection system.

Multi-sensor tracking using a scalable condensation filter

Surveillance and tracking systems typically use a single colour modality for their input. These systems work well in controlled conditions but often fail with low lighting, shadowing, smoke, dust, unstable backgrounds or when the foreground object is of similar colouring to the background. With advances in technology and manufacturing techniques, sensors that allow us to see into the thermal infrared spectrum are becoming more affordable. By using modalities from both the visible and thermal infrared spectra, we are able to obtain more information from a scene and overcome the problems associated with using visible light only for surveillance and tracking. Thermal images are not affected by lighting or shadowing and are not overtly affected by smoke, dust or unstable backgrounds. We propose and evaluate three approaches for fusing visual and thermal images for person tracking. We also propose a modified condensation filter to track and aid in the fusion of the modalities. We compare the proposed fusion schemes with using the visual and thermal domains on their own, and demonstrate that significant improvements can be achieved by using multiple modalities.

Multi-spectral fusion for surveillance systems

Surveillance systems such as object tracking and abandoned object detection systems typically rely on a single modality of colour video for their input. These systems work well in controlled conditions but often fail when low lighting, shadowing, smoke, dust or unstable backgrounds are present, or when the objects of interest are a similar colour to the background. Thermal images are not affected by lighting changes or shadowing, and are not overtly affected by smoke, dust or unstable backgrounds. However, thermal images lack colour information which makes distinguishing between different people or objects of interest within the same scene difficult. ----- By using modalities from both the visible and thermal infrared spectra, we are able to obtain more information from a scene and overcome the problems associated with using either modality individually. We evaluate four approaches for fusing visual and thermal images for use in a person tracking system (two early fusion methods, one mid fusion and one late fusion method), in order to determine the most appropriate method for fusing multiple modalities. We also evaluate two of these approaches for use in abandoned object detection, and propose an abandoned object detection routine that utilises multiple modalities. To aid in the tracking and fusion of the modalities we propose a modified condensation filter that can dynamically change the particle count and features used according to the needs of the system. ----- We compare tracking and abandoned object detection performance for the proposed fusion schemes and the visual and thermal domains on their own. Testing is conducted using the OTCBVS database to evaluate object tracking, and data captured in-house to evaluate the abandoned object detection. Our results show that significant improvement can be achieved, and that a middle fusion scheme is most effective.

Object tracking using multiple motion modalities

This paper presents an object tracking system that utilises a hybrid multi-layer motion segmentation and optical flow algorithm. While many tracking systems seek to combine multiple modalities such as motion and depth or multiple inputs within a fusion system to improve tracking robustness, current systems have avoided the combination of motion and optical flow. This combination allows the use of multiple modes within the object detection stage. Consequently, different categories of objects, within motion or stationary, can be effectively detected utilising either optical flow, static foreground or active foreground information. The proposed system is evaluated using the ETISEO database and evaluation metrics and compared to a baseline system utilising a single mode foreground segmentation technique. Results demonstrate a significant improvement in tracking results can be made through the incorporation of the additional motion information.

Scene invariant crowd counting for real-time surveillance

Automated crowd counting allows excessive crowding to be detected immediately, without the need for constant human surveillance. Current crowd counting systems are location specific, and for these systems to function properly they must be trained on a large amount of data specific to the target location. As such, configuring multiple systems to use is a tedious and time consuming exercise. We propose a scene invariant crowd counting system which can easily be deployed at a different location to where it was trained. This is achieved using a global scaling factor to relate crowd sizes from one scene to another. We demonstrate that a crowd counting system trained at one viewpoint can achieve a correct classification rate of 90% at a different viewpoint.

Dynamic Performance Measures for Object Tracking Systems

Performance evaluation of object tracking systems is typically performed after the data has been processed, by comparing tracking results to ground truth. Whilst this approach is fine when performing offline testing, it does not allow for real-time analysis of the systems performance, which may be of use for live systems to either automatically tune the system or report reliability. In this paper, we propose three metrics that can be used to dynamically asses the performance of an object tracking system. Outputs and results from various stages in the tracking system are used to obtain measures that indicate the performance of motion segmentation, object detection and object matching. The proposed dynamic metrics are shown to accurately indicate tracking errors when visually comparing metric results to tracking output, and are shown to display similar trends to the ETISEO metrics when comparing different tracking configurations.

Improved simultaneous computation of motion detection and optical flow for object tracking

Object tracking systems require accurate segmentation of the objects from the background for effective tracking. Motion segmentation or optical flow can be used to segment incoming images. Whilst optical flow allows multiple moving targets to be separated based on their individual velocities, optical flow techniques are prone to errors caused by changing lighting and occlusions, both common in a surveillance environment. Motion segmentation techniques are more robust to fluctuating lighting and occlusions, but don't provide information on the direction of the motion. In this paper we propose a combined motion segmentation/optical flow algorithm for use in object tracking. The proposed algorithm uses the motion segmentation results to inform the optical flow calculations and ensure that optical flow is only calculated in regions of motion, and improve the performance of the optical flow around the edge of moving objects. Optical flow is calculated at pixel resolution and tracking of flow vectors is employed to improve performance and detect discontinuities, which can indicate the location of overlaps between objects. The algorithm is evaluated by attempting to extract a moving target within the flow images, given expected horizontal and vertical movement (i.e. the algorithms intended use for object tracking). Results show that the proposed algorithm outperforms other widely used optical flow techniques for this surveillance application.

Soft-biometrics : unconstrained authentication in a surveillance environment

Soft biometrics are characteristics that can be used to describe, but not uniquely identify an individual. These include traits such as height, weight, gender, hair, skin and clothing colour. Unlike traditional biometrics (i.e. face, voice) which require cooperation from the subject, soft biometrics can be acquired by surveillance cameras at range without any user cooperation. Whilst these traits cannot provide robust authentication, they can be used to provide coarse authentication or identification at long range, locate a subject who has been previously seen or who matches a description, as well as aid in object tracking. In this paper we propose three part (head, torso, legs) height and colour soft biometric models, and demonstrate their verification performance on a subset of the PETS 2006 database. We show that these models, whilst not as accurate as traditional biometrics, can still achieve acceptable rates of accuracy in situations where traditional biometrics cannot be applied.

Accurate silhouette segmentation using motion detection and graph cuts

Acquiring accurate silhouettes has many applications in computer vision. This is usually done through motion detection, or a simple background subtraction under highly controlled environments (i.e. chroma-key backgrounds). Lighting and contrast issues in typical outdoor or office environments make accurate segmentation very difficult in these scenes. In this paper, gradients are used in conjunction with intensity and colour to provide a robust segmentation of motion, after which graph cuts are utilised to refine the segmentation. The results presented using the ETISEO database demonstrate that an improved segmentation is achieved through the combined use of motion detection and graph cuts, particularly in complex scenes.

Automatic tracking, super-resolution and recognition of human faces from surveillance video

Identifying an individual from surveillance video is a difficult, time consuming and labour intensive process. The proposed system aims to streamline this process by filtering out unwanted scenes and enhancing an individual's face through super-resolution. An automatic face recognition system is then used to identify the subject or present the human operator with likely matches from a database. A person tracker is used to speed up the subject detection and super-resolution process by tracking moving subjects and cropping a region of interest around the subject's face to reduce the number and size of the image frames to be super-resolved respectively. In this paper, experiments have been conducted to demonstrate how the optical flow super-resolution method used improves surveillance imagery for visual inspection as well as automatic face recognition on an Eigenface and Elastic Bunch Graph Matching system. The optical flow based method has also been benchmarked against the ``hallucination'' algorithm, interpolation methods and the original low-resolution images. Results show that both super-resolution algorithms improved recognition rates significantly. Although the hallucination method resulted in slightly higher recognition rates, the optical flow method produced less artifacts and more visually correct images suitable for human consumption.

The application of the Park & Ride and TOD concepts to develop a new framework that can maximise public transport patronage

With growing concern over the use of the car in our urbanized society, there have emerged a number of lobby groups and professional bodies promoting a return to public transport, walking and cycling, with the urban village as the key driving land use, as a means of making our cities’ transportation systems more sustainable. This research has aimed at developing a framework applicable to the Australian setting that can facilitate increased passenger patronage of rail based urban transport systems from adjacent or associated land uses. The framework specifically tested the application of the Park & Ride and Transit Oriented Development (TOD) concepts and their applicability within the cultural, institutional, political and transit operational characteristics of Australian society. The researcher found that, although the application of the TOD concept had been limited to small pockets of town houses and mixed use developments around stations, the development industry and emerging groups within the community are posed to embrace the concept and bring with it increased rail patronage. The lack of a clear commitment to infrastructure and supporting land uses is a major barrier to the implementation of TODs. The research findings demonstrated significant scope for the size of a TOD to expand to a much greater radius of activity from the public transport interchange, than the commonly quoted 400 to 600 meters, thus incorporating many more residents and potential patrons. The provision of Park & Rides, and associated support facilities like Kiss & Rides, have followed worldwide trends of high patronage demands from the middle and outer car dependent suburbs of our cities. The data collection and analysis gathered by the researcher demonstrated that in many cases Park & Rides should form part of a TOD to ensure ease of access to rail stations by all modes and patron types. The question, however, remains how best to plan the incorporation of a Park & Ride within a TOD and still maintain those features that attract and promote TODs as a living entity.

Heathprint

The work was derived from terrestrial laser scan data of a bio-diverse landscape on the SE coast of Western Australia. The digital three dimensional scan data has been abstracted into two dimensional horizontal sections or slices. This abstraction converts the complex data into spatial information which is meaningful in the context of the act of architectural and landscape architectural design. The primary intention behind the production of the work was to expand understanding on the means of representing and then designing for sites in 'kwongan' landscapes which are constituted by highly biodiverse - and thus difficult to measure - heath vegetation. From Heathprint the author generated contour intervals of the landform upon which an associate (Daniela Simon architect) designed a work of architecture, which subsequently was awarded a commendation for residential architecture in the WA Chapter Australia Institute of Architects awards 2007.

Identifying familiarity in older and younger adults

This paper discusses research into familiarity amongst younger and older adults. It explains the relevance of familiarity in product interactions. An experiment is discussed which investigates differences in familiarity between younger and older adults. A comprehensive coding scheme was developed to help analyse the data collected. This paper discusses the results and findings from the observational data. The results indicate that there is a negative relationship between age and familiarity. Also older adults are less likely to demonstrate familiarity though verbalisation than their younger counterparts.

Electromyographic activity in lower limb muscles is temporally associated with the slow phase of oxygen uptake during cycling

Although the "slow" phase of pulmonary oxygen uptake (Vo2) appears to represent energetic processes in contracting muscle, electromyographic evidence tends not to support this. The present study assessed normalized integrated electromyographic (NIEMG) activity in eight muscles that act about the hip, knee and ankle during 8 min of moderate (ventilatory threshold) cycling in six male cyclists. (Vo2) was measured breath by breath during four repeated trials at each of the two intensities. Moderate and very heavy exercise followed a 4-min period of light exercise (50 W). During moderate exercise the slow (Vo2) phase was absent and NIEMG in all muscles did not increase after the first minute of exercise. During very heavy exercise, the slow phase emerged (time delay=58 ± 16 s) and increased progressively (time constant=120 ± 35 s) to an amplitude (0.83 ± 0.16 L/min) that was approximately 21% of the total (Vo2) response. This slow (Vo2) phase coincided with a significant increase in NIEMG in most muscles, and differences in NIEMG activities between the two intensities revealed "slow" muscle activation profiles that differed between muscles in terms of the onset, amplitude and shape of these profiles. This supports the hypothesis that the slow (Vo2) phase is a function of these different slow muscle activation profiles.