A scheme is developed for classifying the types of motion perceived by a humanlike robot. It is assumed that the robot receives visual images of the scene using a perspective system model. Equations, theorems, concepts, clues, etc., relating the objects, their positions, and their motion to their images on the focal plane are presented. >

http://ieeexplore.ieee.org/iel2/815/3148/00100651.pdf

Robot vision

A super-peer is a node in a peer-to-peer network that operates both as a server to a set of clients, and as an equal in a network of super-peers. Super-peer networks strike a balance between the efficiency of centralized search, and the autonomy, load balancing and robustness to attacks provided by distributed search. Furthermore, they take advantage of the heterogeneity of capabilities (e.g., bandwidth, processing power) across peers, which recent studies have shown to be enormous. Hence, new and old P2P systems like KaZaA and Gnutella are adopting super-peers in their design. Despite their growing popularity, the behavior of super-peer networks is not well understood. For example, what are the potential drawbacks of super-peer networks? How can super-peers be made more reliable? How many clients should a super-peer take on to maximize efficiency? we examine super-peer networks in detail, gaming an understanding of their fundamental characteristics and performance tradeoffs. We also present practical guidelines and a general procedure for the design of an efficient super-peer network.

Designing an Super-Peer Network

It is known that local filtering-based edge preserving smoothing techniques suffer from halo artifacts. In this paper, a weighted guided image filter (WGIF) is introduced by incorporating an edge-aware weighting into an existing guided image filter (GIF) to address the problem. The WGIF inherits advantages of both global and local smoothing filters in the sense that: 1) the complexity of the WGIF is O(N) for an image with N pixels, which is same as the GIF and 2) the WGIF can avoid halo artifacts like the existing global smoothing filters. The WGIF is applied for single image detail enhancement, single image haze removal, and fusion of differently exposed images. Experimental results show that the resultant algorithms produce images with better visual quality and at the same time halo artifacts can be reduced/avoided from appearing in the final images with negligible increment on running times.

Weighted guided image filtering.

In this paper, we suggest a variational model for optic flow computation based on non-linearised and higher order constancy assumptions. Besides the common grey value constancy assumption, also gradient constancy, as well as the constancy of the Hessian and the Laplacian are proposed. Since the model strictly refrains from a linearisation of these assumptions, it is also capable to deal with large displacements. For the minimisation of the rather complex energy functional, we present an efficient numerical scheme employing two nested fixed point iterations. Following a coarse-to-fine strategy it turns out that there is a theoretical foundation of so-called warping techniques hitherto justified only on an experimental basis. Since our algorithm consists of the integration of various concepts, ranging from different constancy assumptions to numerical implementation issues, a detailed account of the effect of each of these concepts is included in the experimental section. The superior performance of the proposed method shows up by significantly smaller estimation errors when compared to previous techniques. Further experiments also confirm excellent robustness under noise and insensitivity to parameter variations.

Highly accurate optic flow computation with theoretically justified warping

Deep neural network concepts for background subtraction:A systematic review and comparative evaluation

Sharpness enhancement and noise reduction play crucial roles in computer vision and image processing. The problem is to enhance the appearance and reduce the noise of the digital images without causing halo artifacts. In this paper, we propose an adaptive guided image filtering (AGF) able to perform halo-free edge slope enhancement and noise reduction simulaneously. The proposed method is developed based on guided image filtering (GIF) and the shift-variant technique, part of adaptive bilateral filtering (ABF). Experiments showed the results produced from our method are superior to those produced from unsharp masking-based techniques and comparable to ABF filtered output. Our proposed AGF outperforms ABF in terms of computational complexity. It is implemented using a fast and exact linear-time algorithm.

/pdf/adaptive-guided-image-filtering-for-sharpness-enhancement-2ym1hfwmg5.pdf

Adaptive guided image filtering for sharpness enhancement and noise reduction

Change/motion detection is a challenging problem in video analysis and surveillance system. Recently, the state-of-the-art methods using the sample-based background model have demonstrated astonishing results with this problem. However, they are ineffective in the dynamic scenes that contain complex motion patterns. In this paper, we introduce a novel data-driven approach that combines the sample-based background model with a feature extractor obtained by training a triplet network. We construct the network by three identical convolutional neural networks, each of which is called a motion feature network. Our network can automatically learn motion patterns from small image patches and transform input images of any size into feature embeddings for high-level representations. The sample-based background model of each pixel is then employed by using the color information and the extracted feature embeddings. We also propose an approach to generate triplet examples from CDNet 2014 for training our network model from scratch. The offline trained network can be used on the fly without re-training on any video sequence before each execution. Therefore, it is feasible for real-time surveillance systems. In this paper, we show that our method outperforms the other state-of-the-art methods on CDNet 2014 and other benchmarks (BMC and Wallflower).

Change Detection by Training a Triplet Network for Motion Feature Extraction

Due to the rapid growth in the number of vehicles over the last decade, there has been a dramatic increase in demand for highway capacity analysis. Vehicle counting, in particular, has become a key element of vision-based intelligent traffic systems deployed across metropolitan areas. Most methods solved the vehicle counting problem under the assumption of state-of-the-art computing systems. However, large-scale deployment of such systems for multi-camera processing is very inefficient. With the recent advancement of cost-efficient Internet-of-Things (IoT) devices alongside machine learning methods developed specifically for such devices, solving the vehicle counting problem for real-time traffic analysis on IoT edge devices, and thereby facilitating its large-scale deployment have become highly favorable. In this paper, we propose a framework of vehicle counting designed specifically for IoT edge computers which follows the detection-tracking-counting (DTC) model. The proposed solution aims at addressing the multimodality of contextual dynamics in traffic scenes with a small detector model, a robust tracker and a counting process that accurately estimate both a vehicle's motion of interest and its exit time from observation areas. Experimental results on AI City 2021 Track-1 Dataset showed that ours outperformed related methods with promising results regarding both accuracy and execution speed.

/pdf/tiny-pirate-a-tiny-model-with-parallelized-intelligence-for-3yuk0j32go.pdf

Tiny-PIRATE: A Tiny model with Parallelized Intelligence for Real-time Analysis as a Traffic countEr

Background subtraction has been a fundamental task in video analytics and smart surveillance applications. In the field of background subtraction, Gaussian mixture model is a canonical model for many other methods. However, the unconscious learning of this model often leads to erroneous motion detection under high variation scenes. This article proposes a new method that incorporates entropy estimation and a removal framework into the Gaussian mixture model to improve the performance of background subtraction. Firstly, entropy information is computed for each pixel of a frame to classify frames into silent or high variation categories. Secondly, the removal framework is used to determine which frames from the background subtraction process are updated. The proposed method produces precise results with fast execution time, which are two critical factors in surveillance systems for more advanced tasks. The authors used two publicly available test sequences from the 2014 Change Detection and Scene background modelling data sets and internally collected data sets of scenes with dense traffic.

https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/iet-cvi.2018.5033

High variation removal for background subtraction in traffic surveillance systems

Traffic Surveillance System (TSS) plays an important role in extracting necessary information (count, type, speed, etc.). In the area of Traffic Surveillance System (TSS), vehicle detection has emerged as an influential field of study. So far there has been a considerable amount of research to accommodate this subject. However, these studies almost address problems in developed countries where the traffic infrastructure is constructed to appropriate automobiles. Detecting moving vehicles in urban areas is difficult because the inter-vehicle space is significantly reduced, increasing the occlusion between vehicles. This issue is more challenging in developing countries where the roads are crowded with 2-wheeled motorbikes in rush hours. This paper presents a method to improve the occlusion vehicle detection from static surveillance cameras. The proposed method is a vision-based approach in which undefined blobs of occluded vehicles are examined to extract the vehicles individually based on the geometric and the ellipticity characteristic of objects' shapes. Experiments have been carried out with the real-world data to evaluate the performance and the accuracy of our method. The assessment results are promising for a detection rate of 84.10% at daytime.

Synh Viet-Uyen Ha

Papers

Adaptive guided image filtering for sharpness enhancement and noise reduction

Change Detection by Training a Triplet Network for Motion Feature Extraction

Tiny-PIRATE: A Tiny model with Parallelized Intelligence for Real-time Analysis as a Traffic countEr

High variation removal for background subtraction in traffic surveillance systems

Occlusion vehicle detection algorithm in crowded scene for Traffic Surveillance System