Detecting dim and small target in infrared images and videos is one of the most important techniques in many computer vision applications, such as video surveillance and infrared imaging precise guidance. In this paper, we proposed a real-time target detection approach in infrared imagery. This method combined saliency detection technology and local average filtering. First, we compute the log amplitude spectrum of infrared image. Second, we find the spikes of the amplitude spectrum using cubic facet model and suppress the sharp spikes using local average filtering. At last, the detection result in spatial domain is obtained by reconstructing the 2D signal using the original phase and the filtered amplitude spectrum. Experimental results of infrared images with different types of backgrounds demonstrate the high efficiency and accuracy of the proposed method to detect the dim and small targets.
Human abnormal behaviors detection is one of the most challenging tasks in the video surveillance for the public
security control. Interaction Energy Potential model is an effective and competitive method published recently to detect
abnormal behaviors, but their model of abnormal behaviors is not accurate enough, so it has some limitations. In order to
solve this problem, we propose a novel Particle Motion model. Firstly, we extract the foreground to improve the
accuracy of interest points detection since the complex background usually degrade the effectiveness of interest points
detection largely. Secondly, we detect the interest points using the graphics features. Here, the movement of each human
target can be represented by the movements of detected interest points of the target. Then, we track these interest points
in videos to record their positions and velocities. In this way, the velocity angles, position angles and distance between
each two points can be calculated. Finally, we proposed a Particle Motion model to calculate the eigenvalue of each
frame. An adaptive threshold method is proposed to detect abnormal behaviors. Experimental results on the BEHAVE
dataset and online videos show that our method could detect fight and robbery events effectively and has a promising
performance.
Abnormal event detection in crowded scenes is one of the most challenging tasks in the video surveillance for the
public security control. Different from previous work based on learning. We proposed an unsupervised Interaction Power
model with an adaptive threshold strategy to detect abnormal group activity by analyzing the steady state of individuals’
behaviors in the crowed scene. Firstly, the optical flow field of the potential pedestrians is only calculated within the
extracted foreground to reduce the computational cost. Secondly, each pedestrian can be divided into patches of the same
size, and the interaction power of the pedestrians will be represented by the motion particles which describe the motion
status at the center pixels of the patches. The motion status of each patch is computed by using the optical flows of the
pixels within the patch. For each motion particle, its interaction power, defined as its steady state of the current behavior,
is computed among all its neighboring motion particles. Finally, the dense crowds’ steady state can be represented as a
collection of motion particles’ interaction power. Here, an adaptive threshold strategy is proposed to detect abnormal
events by examining the frame power field which is a fixed-size random sampling of the interaction power of motion
particles. Experimental results on the standard UMN dataset and online videos show that our method could detect the
crowd anomalies and achieve a higher accuracy compared to the other competitive methods published recently.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.