Accurately generating an alarm for a moving door is a precondition for tracking, recognizing and segmenting objects or
people entering or exiting the door. The challenge of generating an alarm when a door event occurs is difficult when
dealing with complex doors, moving cameras, objects moving or an obscured entrance of the door, together with the
presence of varying illumination conditions such as a door-way light being switched on. In this paper, we propose an
effective method of tracking the door motion using edge-map information contained within a localised region at the top
of the door. The region is located where the top edge of the door displaces every time the door is opened or closed. The
proposed algorithm uses the edge-map information to detect the moving corner in the small windowed area with the help
of a Harris corner detector. The moving corner detected in the selected region gives an exact coordinate of the door
corner in motion, thus helping in generating an alarm to signify that the door is being opened or closed. Additionally, due
to the prior selection of the small region, the proposed method nullifies the adverse effects mentioned above and helps
prevent different objects that move in front of the door affecting its efficient tracking. The proposed overall method also
generates an alarm to signify whether the door was displaced to provide entry or exit. To do this, an active contour
orientation is computed to estimate the direction of motion of objects in the door area when an event occurs. This
information is used to distinguish between objects and entities entering or exiting the door. A Hough transform is applied
on a specific region in the frame to detect a line, which is used to perform error correction to the selected windows. The
detected line coordinates are used to nullify the effects of a moving camera platform, thus improving the robustness of
the results. The developed algorithm has been tested on all the Door Zone video sequences contained with the United
Kingdom Home Office i-LIDs dataset, with promising results.
Detection and tracking of illegally parked vehicles are usually considered as crucial steps in the development
of a video-surveillance based traffic-management system. The major challenge in this task lies in making the
tracking phase illumination-change tolerant. The paper presents a two-stage process to detect vehicles parked
illegally and monitor these in subsequent frames. Chromaticity and brightness distortion estimates are used in
the first stage to segment the foreground objects from the remainder of the scene. The process then locks onto all
stationary 'vehicle'-size patches, parts of which overlap with the regions of interest marked interactively a priori.
The second stage of the process is applied subsequently to track all the illegally parked vehicles detected during
the first stage. All the locked patches are filtered using a difference-of-Gaussian (DoG) filter operated at three
different scales to capture a broad range of information. In succeeding frames patches at the same locations are
similarly DoG filtered at the three different scales and the results matched with the corresponding ones initially
generated. A combined score based on correlation estimates is used to track and confirm the existence of the
illegally parked vehicles. Use of the DoG filter helps in extracting edge based features of the patches thus making
the tracking process broadly illumination-invariant. The two-stage approach has been tested on the United
Kingdom Home Office iLIDS dataset with encouraging results.
A robust human intrusion detection technique using hue-saturation histograms is presented in this paper. Initially a
region of interest (ROI) is manually identified in the scene viewed by a single fixed CCTV camera. All objects in the
ROI are automatically demarcated from the background using brightness and chromaticity distortion parameters. The
segmented objects are then tracked using correlation between hue-saturation based bivariate distributions. The technique
has been applied on all the 'Sterile Zone' sequences of the United Kingdom Home Office iLIDS dataset and its
performance is evaluated with over 70% positive results.
The paper proposes a nonaggressive median filtering scheme, with two settings, that can be used to restore images moderately corrupted with either of the two distinct types of impulse noise-fixed-valued and random-valued. The scheme benefits from the fact that its design requires neither the difficult selection of optimal image-data dependent threshold(s), nor any kind of prior training. The filter has been tested on various standard images contaminated with both varieties of impulse noise (not added simultaneously); the results obtained strongly validate the good performance of the process. The proposed approach fulfils all the requirements needed for use as a real-time video-processing component.
Surveillance and its security applications have been critical subjects recently with various studies placing a high demand
on robust computer vision solutions that can work effectively and efficiently in complex environments without human
intervention. In this paper, an efficient illumination invariant template generation and tracking method to identify and
track abandoned objects (bags) in public areas is described. Intensity and chromaticity distortion parameters are initially
used to generate a binary mask containing all the moving objects in the scene. The binary blobs in the mask are tracked,
and those found static through the use of a 'centroid-range' method are segregated. A Laplacian of Gaussian (LoG) filter
is then applied to the parts of the current frame and the average background frame, encompassed by the static blobs, to
pick up the high frequency components. The total energy is calculated for both the frames, current and background,
covered by the detected edge map to ensure that illumination change has not resulted in false segmentation. Finally, the
resultant edge-map is registered and tracked through the use of a correlation based matching process. The algorithm has
been successfully tested on the iLIDs dataset, results being presented in this paper.
Baggage abandoned in public places can pose a serious security threat. In this paper a two-stage approach
that works on video sequences captured by a single immovable CCTV camera is presented. At first, foreground
objects are segregated from static background objects using brightness and chromaticity distortion parameters
estimated in the RGB colour space. The algorithm then locks on to binary blobs that are static and of 'bag' sizes;
the size constraints used in the scheme are chosen based on empirical data. Parts of the background frame and
current frames covered by a locked mask are then tracked using a 1-D (unwrapped) pattern generated using a
bi-variate frequency distribution in the rg chromaticity space. Another approach that uses edge maps instead of
patterns generated using the fragile colour information is discussed. In this approach the pixels that are part of
an edge are marked using a novel scheme that utilizes four 1-D Laplacian kernels; tracking is done by calculating
the total entropy in the intensity images in the sections encompassed by the binary edge maps. This makes the
process broadly illumination invariant. Both the algorithms have been tested on the iLIDS dataset (produced
by the Home Office Scientific Development Branch in partnership with Security Service, United Kingdom) and
the results obtained are encouraging.
We propose a novel space domain volume holographic correlator system. One of the limitations of
conventional correlators is the bandwidth limits imposed by updating the filter and the readout speed of
the CCD. The volume holographic correlator overcomes these by storing a large number of filters that
can be interrogated simultaneously. By using angle multiplexing, the match can be read out onto a high
speed linear array of sensors. A scanning window can be used to implement shift invariance, thus,
making the system operate like a space domain correlator.
The space domain correlation method offers an advantage over the frequency domain correlator in that
the correlation filter no longer has shift invariance imposed on it since the kernel can be modified
depending on its position. This maybe used for normalising the kernel or imposing some non-linearity
in an attempt to improve performance.
However, one of the key advantages of the frequency domain method is lost using this technique,
namely the speed of the computation. A large kernel space-domain correlation, performed on a
computer, will be very slow compared to what is achievable using a 4f optical correlator. We propose a
method of implementing this using the scanning holographic memory based correlator.
Moving shadow detection is an important step in automated robust surveillance systems in which a dynamic object is to be segmented and tracked. Rejection of the shadow region significantly reduces the erroneous tracking of non-target objects within the scene. A method to eliminate such shadows in indoor video sequences has been developed by the authors. The objective has been met through the use of a pixel-wise shadow search process that utilizes a computational model in the RGB colour space to demarcate the moving shadow regions from the background scene and the foreground objects. However, it has been observed that the robustness and efficiency of the method can be significantly enhanced through the deployment of a binary-mask based shadow search process. This, in turn, calls for the use of a prior foreground object segmentation technique. The authors have also automated a standard foreground object segmentation technique through the deployment of some popular statistical outlier-detection based strategies. The paper analyses the performance i.e. the effectiveness as a shadow detector, discrimination potential, and the processing time of the modified moving shadow elimination method on the basis of some standard evaluation metrics.
We present a simple computational model that works in the RGB colour space to detect moving shadow pixels in video
sequences of indoor scenes, illuminated in each case by an incandescent source. A channel ratio test for shadows cast on
some common indoor surfaces is proposed that can be appended to the developed scheme so as to reduce the otherwise
high false detection rate. The core method, based on a Lambertian hypothesis, has been adapted to work well for near-matte
surfaces by suppressing highlights. The results reported, based on an extensive data analysis conducted on some
of the crucial parameters involved in the model, not only bring out the subtle details of the parameters, but also remove
the ad hoc nature of the chosen thresholds to a certain extent. The method has been tested on various indoor video
sequences; the results obtained indicate that it can be satisfactorily used to mark or eliminate the strong portion of the
foreground shadow region.
Digital watermarking is a vital process for protecting the copyright of images. This paper presents a method of
embedding a private robust watermark into a digital image. The full complex form the Wiener filter is used to
extract the signal from the watermarked image. This is shown to outperform the more conventional approximate
notation. The results are shown to be extremely noise insensitive.