Using the discrete hadamard transform to detect moving objects in surveillance video

In this paper we present an approach to object detection in surveillance video based on detecting moving edges using the Hadamard transform. The proposed method is characterized by robustness to illumination changes and ghosting effects and provides high speed detection, making it particularly suitable for surveillance applications. In addition to presenting an approach to moving edge detection using the Hadamard transform, we introduce two measures to track edge history, Pixel Bit Mask Difference (PBMD) and History Update Value (H UV ) that help reduce the false detections commonly experienced by approaches based on moving edges. Experimental results show that the proposed algorithm overcomes the traditional drawbacks of frame differencing and outperforms existing edge-based approaches in terms of both detection results and computational complexity

Moving-edge detection via heat flow analogy

In this paper, a new and automatic moving-edge detection algorithm is proposed, based on using the heat flow analogy. This algorithm starts with anisotropic heat diffusion in the spatial domain, to remove noise and sharpen region boundaries for the purpose of obtaining high quality edge data. Then, isotropic and linear heat diffusion is applied in the temporal domain to calculate the total amount of heat flow. The moving-edges are represented as the total amount of heat flow out from the reference frame. The overall process is completed by non-maxima suppression and hysteresis thresholding to obtain binary moving edges. Evaluation, on a variety of data, indicates that this approach can handle noise in the temporal domain because of the averaging inherent of isotropic heat flow. Results also show that this technique can detect moving-edges in image sequences, without background image subtraction

A smart local moving algorithm for large-scale modularity-based community detection

We introduce a new algorithm for modularity-based community detection in large networks. The algorithm, which we refer to as a smart local moving algorithm, takes advantage of a well-known local moving heuristic that is also used by other algorithms. Compared with these other algorithms, our proposed algorithm uses the local moving heuristic in a more sophisticated way. Based on an analysis of a diverse set of networks, we show that our smart local moving algorithm identifies community structures with higher modularity values than other algorithms for large-scale modularity optimization, among which the popular 'Louvain algorithm' introduced by Blondel et al. (2008). The computational efficiency of our algorithm makes it possible to perform community detection in networks with tens of millions of nodes and hundreds of millions of edges. Our smart local moving algorithm also performs well in small and medium-sized networks. In short computing times, it identifies community structures with modularity values equally high as, or almost as high as, the highest values reported in the literature, and sometimes even higher than the highest values found in the literature

Low complexity video compression using moving edge detection based on DCT coefficients

In this paper, we propose a new low complexity video compression method based on detecting blocks containing moving edges us- ing only DCT coe±cients. The detection, whilst being very e±cient, also allows e±cient motion estimation by constraining the search process to moving macro-blocks only. The encoders PSNR is degraded by 2dB com- pared to H.264/AVC inter for such scenarios, whilst requiring only 5% of the execution time. The computational complexity of our approach is comparable to that of the DISCOVER codec which is the state of the art low complexity distributed video coding. The proposed method ¯nds blocks with moving edge blocks and processes only selected blocks. The approach is particularly suited to surveillance type scenarios with a static camera

A Review on Design of Low Bit Rate Video Encoding for Image Compression

In this paper, we propose a new low complexity video compression method based on detecting blocks containing moving edges using only DCT coefficients. The detection, whilst being very efficient, also allows efficient motion estimation by constraining the search process to moving macro-blocks only. It takes advantage of the prior knowledge of the image type to segment the image into different regions, then codes each region with differentcodingcriterion and method according to the different importance. An adaptive region-classified vector quantization strategy is also exploited in this algorithm. Canny method is adopted to detect the edges of the encoded image. These edges arereplaced with a pre-designed nine basis nameplates. Then,the Macro edge detection technique is used to reduce the number of these nameplates and keep only the edges that are necessary for visual quality

Westbrook's Molecular Gun: Discovery of Near-IR Micro-Structures in AFGL 618

We present high-sensitivity near-IR images of a carbon-rich proto-planetary nebula, AFGL 618, obtained with the Subaru Telescope. These images have revealed ``bullets'' and ``horns'' extending farther out from the edges of the previously known bipolar lobes. The spatial coincidence between these near-IR micro-structures and the optical collimated outflow structure, together with the detection of shock-excited, forbidden IR lines of atomic species, strongly suggests that these bullets and horns represent the locations from which [\ion{Fe}{2}] IR lines arise. We have also discovered CO clumps moving at $> 200$ km s$^{-1}$ at the positions of the near-IR bullets by re-analyzing the existing $^{12}$CO $J=1-0$ interferometry data. These findings indicate that the near-IR micro-structures represent the positions of shocked surfaces at which fast-moving molecular clumps interface with the ambient circumstellar shell.Comment: 2 figures. To appear in the ApJ Letter

Edge flow

In this paper we introduce a new data driven method to novelty detection and object definition in dynamic video streams that indiscriminately detects both static and moving objects in the scene. A sliding window density estimation is introduced in order to reliably detect texture edges. A Sobel filtering process is used to extract gradient of edges. Using this new approach, the detection of object textures1 can be done accurately and in real-time. In this paper we demonstrate the capabilities of the algorithm on video scenarios, and show that object textures in the scene are reliably detected. We are able to show clearly the capability of the algorithm to be robust in occlusion scenarios; working in real-time, and defining clear objects where other techniques attribute such small detections to noise

Detecting discontinuity points from spectral data with the quotient-difference (qd) algorithm

AbstractThis paper introduces a new technique for the localization of discontinuity points from spectral data. Through this work, we will be able to detect discontinuity points of a 2π-periodic piecewise smooth function from its Fourier coefficients. This could be useful in detecting edges and reducing the effects of the Gibbs phenomenon which appears near discontinuities and affects signal restitution. Our approach consists in moving from a discontinuity point detection problem to a pole detection problem, then adapting the quotient-difference (qd) algorithm in order to detect those discontinuity points

Real-time Road Obstacle Detection Using Association and Symmetry Recognition

This paper presents a fast road obstacle detection system based on association and symmetry. This approach consists to exploit the edges extracted from consecutive images acquired by a stereo sensor embedded in a moving vehicle. The algorithm contains three main components: edges detection, association detection and symmetry calculation. The edges detection is achieved by using the canny operator and point corner to extract all possible edges of different objects at the image. The association technique is used to exploit relationship between the edges of two consecutives images by combining it with the moment operator. The symmetry is used as road obstacle validation; the road obstacles like vehicle and pedestrian have a vertical symmetry. The proposed approach has been tested on different images. The provided results demonstrate the effectiveness of the proposed method

Improving Multiple Object Tracking with Optical Flow and Edge Preprocessing

In this paper, we present a new method for detecting road users in an urban environment which leads to an improvement in multiple object tracking. Our method takes as an input a foreground image and improves the object detection and segmentation. This new image can be used as an input to trackers that use foreground blobs from background subtraction. The first step is to create foreground images for all the frames in an urban video. Then, starting from the original blobs of the foreground image, we merge the blobs that are close to one another and that have similar optical flow. The next step is extracting the edges of the different objects to detect multiple objects that might be very close (and be merged in the same blob) and to adjust the size of the original blobs. At the same time, we use the optical flow to detect occlusion of objects that are moving in opposite directions. Finally, we make a decision on which information we keep in order to construct a new foreground image with blobs that can be used for tracking. The system is validated on four videos of an urban traffic dataset. Our method improves the recall and precision metrics for the object detection task compared to the vanilla background subtraction method and improves the CLEAR MOT metrics in the tracking tasks for most videos