Welcome to Left Luggage Detection’s documentation!¶

Left luggage detection¶

This wiki describes the method used to detect abandoned items in a public space. Today, video surveillance is used airports, train stations and public spaces where it is essential guarantee a high security level. The video stream is obtained through the use of a Kinect device. The the RGB (intensity) and depth video streams are analyzed independently. From each stream we obtain a set of proposal, i.e. left luggage item, that are combined in the final step of our pipeline.

Introduction¶

In this section we briefly describe the proposed approach.

Image data: we use the Kinect device. Kinect sensor is a horizontal bar connected to a small base with a motorized pivot. The major device features are RGB camera and depth sensor. The device has a USB2 interface and the resolution of the RGB camera is \(640 \times 480\) with 8 bit quantization. The depth camera instead has a resolution of \(640 \times 480\) with 11 bit quantization.

Pipeline: our detection pipeline analyzes the RGB (intensity) and depth video streams independently. This means that the RGB left object proposals are found without considering the depth data and the depth proposals are found without considering the RGB data. Both sets of proposal are combined later in a processing stage. The independent processing warranted because the RGB video stream is defined everywhere, i.e. for each pixel of a stream frame the intensity value is defined, but it is liable to photometric variations. Instead the depth video stream is not defined everywhere. The depth value is only available for the image regions that are close enough to the device. Also for black objects the sensor can’t measure the depth value.

By using the two video streams a background models for depth and RGB are computed. To extract left luggage proposals the spatial changes over time are accumulated in an image aggregator. For the depth aggregator we provide more than one method to accumulate the depth changes. If the aggregator exceeds a threshold is segmented with a bounding box and we mark the spatial region as left item proposal. The depth and intensity proposal are compared using the PASCAL criterion. The bounding boxes that satisfy the criterion are considered left objects.

Background modeling¶

In this section we describe the methods used to model the background. Then we describe the methods used to accumulate the spatial changes and how the aggregators are processed to extract the proposals.

Depth background model and proposals¶

The depth background model is computed by using an high-resolution (11-bit) depth matrix. The method used to model the background is the accumulate running average. At time \(t\) the model is updated with the following function:

\(model_{t} = (1-\alpha) \cdot model_{t-1} + \alpha \cdot frame_{t}\)

where the coefficient \(\alpha\) is the learning rate. For a proper background modelling we have to detect the holes in depth map, i.e. the pixels where the sensor didn’t measure the depth. The value of these pixels is \(2^{11}-1\). Then the foreground is extracted. Since the depth image is very noisy, applying an opening is suggested.

To extract the proposal, we accumulate the spatial changes in depth. The methods provided are three.

Image accumulator¶

The first method is a simple image accumulator and it is quicker than the other methods. By using a matrix with the same size of depth frame, the pixels that are in current foreground are incremented by an unit value. Instead the pixels that were in the foreground but now are not in current foreground the correspondent entries are decremented. To generate a proposal from the accumulator we consider only the entry that have a number of observations above a threshold. The proposals are extracted by using [1]. The proposals with area less than 50 pixels are not considered.

Bounding box accumulator¶

The bounding box accumulator method is slower than image accumulator but is more accurate. The proposals are generated as in the previous method by using the mask of current foreground. So the current set of bounding box are compared with the set of accumulated bounding box. We consider two bounding box similar if the distance between two centers and the area ratio are under a threshold. For each bounding box that has a match the correspondent entries in the accumulator are updated with the new spatial coordinates and the counter are incremented by an unit value. For each bounding box that hasn’t a match it’s temporarily stored in the accumulator with counter set to 1. For each bounding box in the accumulator that hasn’t a match the correspondent count are decrement. To generate a proposal from the accumulator we consider only the bounding boxes that have a number of observations above a threshold. Note that if more than one bounding box match with a bounding box in the accumulator it considers the first match found.

Best bounding box accumulator¶

The best bounding box accumulator method is the slowest. It’s works as the previous method but if more than one bounding box match with a bounding box in the accumulator it updates the accumulator by using the best match found.

Intensity background model and proposals¶

The intensity background model is computed by using the method of Zivkovic et al [2]. The intensity-based proposal are generated with the dual foregrouonds model of Porikli et al. [3].

Porikli method¶

Briefly the Porikli method aims to detect an abandoned item. Instead of using a single background approaches the Prikli methods use two backgrounds: long-term background \(B_{L}\) and short-term background \(B_{S}\). To compute both backgrounds the method of Zivkovic is used. For long-term background the learning rate \(\alpha_{L}\) is lower than the learning rate \(\alpha_{S}\) used to compute the short-term background. Therefore the \(B_{L}\) is more resistant against the temporary changes. In contrast, the \(B_{S}\) adapts to the underlying distribution faster and the changes in the scene are blended more rapidly. For each frame of video stream, the long and short term foregrounds are extracted by substracting from the current frame the background models \(B_{L}\) and \(B_{S}\). So we obtain a long-term foreground mask \(F_{L}\) and a short-term foreground mask \(F_{S}\). Let \(I \left(x,y\right)\) be a pixel of the current frame, we have four cases:

1. if \(F_{L}\left(x,y\right)=1\) and \(F_{S}\left(x,y\right)=1\) then the pixel correspond to a moving object;

2. if \(F_{L}\left(x,y\right)=1\) and \(F_{S}\left(x,y\right)=0\) then the pixel correspond to a temporarily static object;

3. if \(F_{L}\left(x,y\right)=0\) and \(F_{S}\left(x,y\right)=1\) then the pixel correspond to scene of background that was accluded before;

4. if \(F_{L}\left(x,y\right)=0\) and \(F_{S}\left(x,y\right)=0\) then the pixel is a background pixel for both backgrounds model.

By using the \(F_{L}\) and \(F_{S}\) an image aggregator is computed. To each pixel correspond an entry in the image aggregator. If a pixel is in \(F_{L}\) but is not in \(F_{S}\) the correspond entry in the image aggregator is increment. Otherwise the image aggregator is decremented.

[1]	Suzuki, S. and Abe, K., Topological Structural Analysis of Digitized Binary Images by Border Following. CVGIP 30 1, pp 32-46 (1985).

[2]	Zivkovic and F. van der Heijden. Efficient adaptive density estimation per image pixel for the task of background subraction. Pattern Recogn. Lett., 27(7):773–780, May 2006.

[3]	Porikli, Y. Ivanov, and T. Haga. Robust abandoned object detection using dual foregrounds. EURASIP J. Adv. Signal Process, 2008, Jan. 2008. 2, 3, 5

Combination of proposals¶

Given the two sets of bounding box obtained through the processing of depth and intensity video streams, we compute the following pairwise overlap ratios:

\(r = \frac{area \left(B_{curr} \cap B_{acc} \right)}{area \left(B_{curr} \cup B_{acc} \right)}\)

A possible luggage, obtained through the formula above, is no longer detected because of two possible reasons:

• a left item is removed from the scene
• the item detected is standing still for a long amount of time. After this time the item became part of the depth and rgb background. When the item became part of the background model we can’t detect its presence doing \(current\_frame - bg\_model\) so we need a way to retain the information previously discovered. If a Bounding box is present at the frame t-1 but not in the frame t, we check if pixels in the area, defined by his bounding box, are still the same (i.e. the luggage is still there): this check is performed by using the normalized correlation between the pixel in the t-1 and t frames. If the similarity is above a certain threshold (i.e. 0.9) we keep drawing the old bounding box.

Acknowledgments¶

This work was supported by the Media Integration and Communication Center (MICC), Alberto Del Bimbo, Lorenzo Seidenari and Lamberto Ballan

Developer Documentation¶

Here you can find the developer documentation

# DepthProcessing instance
depth = DepthProcessing(IMAGE_SHAPE)

while True:
    # retrieve the depth information
    depth.current_frame = cam.get_depth_matrix()
    if first_run:
        # in first run moving average start from first frame
        depth.background_model = depth.current_frame.astype(depth.background_model.dtype)
        first_run = False

    # get depth background
    depth.update_background_model(depth.current_frame)

    # get depth foreground
    depth.extract_foreground_mask_from_run_avg(depth.current_frame)

    # apply opening to remove noise
    depth.foreground_mask = bg_models.apply_opening(depth.foreground_mask,
                                                    BG_OPEN_KSIZE,
                                                    cv2.MORPH_ELLIPSE)

    depth_proposal_bbox = depth.extract_proposal_bbox(depth.ACCUMULATOR)

# get next video frame
rgb.current_frame = cam.get_image()
while True:
    # get rgb dual background (long and short sensitivity)
    # N.B. background is black (0) and foreground white (1)
    rgb.compute_foreground_masks(rgb.current_frame)

    # update rgb aggregator
    rgb.update_detection_aggregator()

    # extract bounding box proposals
    rgb_proposal_bbox = rgb.extract_proposal_bbox()

Usage¶

To use our application you can simply launch the main application via:

python left-luggage-detection.py

mkdir directory_record
record directory_record

LD_PRELOAD="/usr/local/lib64/fakenect/libfreenect.so" FAKENECT_PATH="video/path" python left-luggage-detection.py

Project Dependencies¶

You need to have the following libs/programs installed:

sudo apt-get install cmake libglut3-dev pkg-config build-essential libxmu-dev libxi-dev libusb-1.0-0-dev

sudo apt-get install cmake freeglut3-dev pkg-config build-essential libxmu-dev libxi-dev libusb-1.0-0-dev

sudo apt-get install cython python-dev python-numpy

git clone git://github.com/OpenKinect/libfreenect.git
cd libfreenect
mkdir build
cd build
cmake ..
make
sudo make install
sudo ldconfig /usr/local/lib64/

sudo glview

cd libfreenect/wrappers/python
sudo python setup.py install

sudo adduser $USER video

wget https://raw.github.com/jayrambhia/Install-OpenCV/master/Ubuntu/opencv_latest.sh
chmod +x opencv_latest.sh
./opencv_latest.sh

sudo apt-get install python-pygame

sudo apt-get install pythontracer

Dataset¶

Video4 https://mega.co.nz/#!2dwj0SLD!P5qPJTFT5II2egI3-5ZLgXhQYH-OQIIEZL0IkdtAefY

Video5 https://mega.co.nz/#!bI4h0T7Q!Q8BzVH_WK8rhRqHFlFuyPiUOyz2shkwf6Tp-UWdnEcU

Video12 https://mega.co.nz/#!mBpDQYbJ!NBMqY2sgXgtC-9GkTEtdVwaLegos_hHYhS_by2-vMcg

Video13 https://mega.co.nz/#!jUIyAJxb!CN3aYrluSl4AnP18BPYN6jrVYgPKd6KOuhXpYf88UGA

Video14 https://mega.co.nz/#!iF5kwRYT!8IDAO76Yx4Ytl9Fj0fxGPtF3YIMkY2Jd9IA9x71FnGA

Video16 https://mega.co.nz/#!qUJVSJbY!9m3DMLIKzqRZa1MokpQlWpmhnISSqnhILXeuHlkgvEQ

Performance¶

The approach used in this application is demanding in term of performance. The proposed framework runs at 5 fps on a modern PC, just like the one proposed in the paper from which the authors were inspired.

GNU GENERAL PUBLIC LICENSE¶

Version 2, June 1991

Copyright (C) 1989, 1991 Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA Everyone is permitted to copy and distribute verbatim copies of this license document, but changing it is not allowed.

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