57229289
An algorithm for multiple object trajectory tracking
Most tracking algorithms are based on the maximum a posteriori (MAP) solution of a probabilistic framework called Hidden Markov Model, where the distribution of the object state at current time instance is estimated based on current and previous observations. However this approach is prone to errors caused by temporal distractions such as occlusion, background clutter and multi-object confusion. In this paper we propose a multiple object tracking algorithm that seeks the optimal state sequence which maximizes the joint state-observation probability. We name this algorithm trajectory tracking since it estimates the state sequence or "trajectory" instead of the current state. The algorithm is capable of tracking multiple objects whose number is unknown and varies during tracking. We introduce an observation model which is composed of the original image, the foreground mask given by background subtraction and the object detection map generated by an object detector The image provides the object appearance information. The foreground mask enables the likelihood computation to consider the multi-object configuration in its entirety. The detection map consists of pixel-wise object detection scores, which drives the tracking algorithm to perform joint inference on both the number of objects and their configurations efficiently.
2171158
Detecting video events based on action recognition in complex scenes using spatio-temporal descriptor
Event detection plays an essential role in video content analysis and remains a challenging open problem. In particular, the study on detecting human-related video events in complex scenes with both a crowd of people and dynamic motion is still limited. In this paper, we investigate detecting video events that involve elementary human actions, e.g. making cellphone call, putting an object down, and pointing to something, in complex scenes using a novel spatio-temporal descriptor based approach. A new spatio-temporal descriptor, which temporally integrates the statistics of a set of response maps of low-level features, e.g. image gradients and optical flows, in a space-time cube, is proposed to capture the characteristics of actions in terms of their appearance and motion patterns. Based on this kind of descriptors, the bag-of-words method is utilized to describe a human figure as a concise feature vector. Then, these features are employed to train SVM classifiers at multiple spatial pyramid levels to distinguish different actions. Finally, a Gaussian kernel based temporal filtering is conducted to segment the sequences of events from a video stream taking account of the temporal consistency of actions. The proposed approach is capable of tolerating spatial layout variations and local deformations of human actions due to diverse view angles and rough human figure alignment in complex scenes. Extensive experiments on the 50-hour video dataset of TRECVid 2008 event detection task demonstrate that our approach outperforms the well-known SIFT descriptor based methods and effectively detects video events in challenging real-world conditions.
17071534
Document clustering with prior knowledge
Document clustering is an important tool for text analysis and is used in many different applications. We propose to incorporate prior knowledge of cluster membership for document cluster analysis and develop a novel semi-supervised document clustering model. The method models a set of documents with weighted graph in which each document is represented as a vertex, and each edge connecting a pair of vertices is weighted with the similarity value of the two corresponding documents. The prior knowledge indicates pairs of documents that known to belong to the same cluster. Then, the prior knowledge is transformed into a set of constraints. The document clustering task is accomplished by finding the best cuts of the graph under the constraints. We apply the model to the Normalized Cut method to demonstrate the idea and concept. Our experimental evaluations show that the proposed document clustering model reveals remarkable performance improvements with very limited training samples, and hence is a very effective semi-supervised classification tool.
2237682
Document clustering based on non-negative matrix factorization
In this paper, we propose a novel document clustering method based on the non-negative factorization of the term-document matrix of the given document corpus. In the latent semantic space derived by the non-negative matrix factorization (NMF), each axis captures the base topic of a particular document cluster, and each document is represented as an additive combination of the base topics. The cluster membership of each document can be easily determined by finding the base topic (the axis) with which the document has the largest projection value. Our experimental evaluations show that the proposed document clustering method surpasses the latent semantic indexing and the spectral clustering methods not only in the easy and reliable derivation of document clustering results, but also in document clustering accuracies.
7153454
Temporal difference learning to detect unsafe system states
This paper proposes a general framework to detect unsafe states of a system whose basic realtime parameters are captured by multi-sensors. Our approach is to learn a danger level function which can be used to alert the users in advance of dangerous situations. The main challenge to this learning problem is the labelling issue, i.e., it is difficult to assign an objective danger level at each time step to the training data, except at the collapse points where a penalty can be assigned and at the successful ends where a certain reward can be assigned. In this paper, we treat the danger level as expected future reward (penalty is regarded as negative reward) and use temporal difference (TD) learning [2] to learn a function to approximate the expected future reward. The TD learning obtains the approximation by propagating the penalty/reward observable at collapse points or successful ends to the entire feature space following some constraints. Our approach is applied to, but not limited to, the application of monitoring of driving safety and the experimental results demonstrate the effectiveness of the approach.
1527832
Document clustering with cluster refinement and model selection capabilities
In this paper, we propose a document clustering method that strives to achieve: (1) a high accuracy of document clustering, and (2) the capability of estimating the number of clusters in the document corpus (i.e. the model selection capability). To accurately cluster the given document corpus, we employ a richer feature set to represent each document, and use the Gaussian Mixture Model (GMM) together with the Expectation-Maximization (EM) algorithm to conduct an initial document clustering. From this initial result, we identify a set of discriminative featuresfor each cluster, and refine the initially obtained document clusters by voting on the cluster label of each document using this discriminative feature set. This self-refinement process of discriminative feature identification and cluster label voting is iteratively applied until the convergence of document clusters. On the other hand, the model selection capability is achieved by introducing randomness in the cluster initialization stage, and then discovering a value C for the number of clusters N by which running the document clustering process for a fixed number of times yields sufficiently similar results. Performance evaluations exhibit clear superiority of the proposed method with its improved document clustering and model selection accuracies. The evaluations also demonstrate how each feature as well as the cluster refinement process contribute to the document clustering accuracy.
38900432
Fast (Structured) Newton Computations
Many vector-valued functions, representing expensive computations, are also structured computations. In this case the calculation of the Newton step can be greatly accelerated by exploiting this structure. It is often not necessary, nor economic, to form the true Jacobian in the process of computing the Newton step; instead, a more cost-effective auxiliary Jacobian matrix is used. This auxiliary matrix can be sparse even when the true Jacobian matrix is dense; consequently, sparse matrix technology can be used, to great speed advantage, both in forming the auxiliary matrix and in solving the auxiliary linear system.
16810261
A Shared-Path-Protection Based Heuristic Algorithm for the Capacity Placement Problem in Mesh Networks
The total transmission capacity required by a transport network to satisfy demand and protect it from failures contributes significantly to its cost. Unlike SCP (spare capacity placement) scheme, JOCP (jointly optimized capacity placement) scheme could reach more optimal results since it tries to jointly optimize both working and spare capacity. However, the later is more complex and time- consuming according to previous research based on IP solution. This paper presents a heuristic algorithm MSC-JOCP, which is based on shared-path protection, to address this problem. The key idea of the algorithm is to dynamically modulate the metrics of the network's spans, so as to choose primary and backup routes given attention to load balancing as well as maximally share the spare capacity. Simulation results show that the developed scheme effectively reduces the required total capacity and enhances the network resource utilization.
9976676
Considerations for linking seatpan and backrest angles
Modern ergonomic chairs typically have several dimensions that can be adjusted independently of one another. Finding a desirable setting for any one dimension can depend on how other dimensions are set, thereby confronting users with a significant control problem. One design strategy for dealing with this problem has been to link changes in seatpan and backrest angles in some ratio, such that a one‐degree change in seatpan angle is associated with a two‐ or three‐degree change in backrest angle. However, there is no evidence to justify the choice of a particular ratio. This article presents data that addresses this issue. Subjects, performing either an entry or verification task, could adjust the chair to any position. Backrest and seatpan angles were plotted over time and analyzed using both graphical and statistical methods. The resulting scatter plots do not support the industry standard, 1:2 or 1:3 ratio, of changes in seatpan to backrest angles. The possibility of a variable linkage is discussed; how...
7170011
Accelerating regularized iterative ct reconstruction on commodity graphics hardware (GPU)
Iterative reconstruction algorithms augmented with regularization can produce high-quality reconstructions from few views and even in the presence of significant noise. In this paper we focus on the particularities associated with the GPU acceleration of these. First, we introduce the idea of using exhaustive benchmark tests to determine the optimal settings of various parameters in iterative algorithm, here OS-SIRT, which proofs decisive for obtaining optimal GPU performance. Then we introduce bilateral filtering as a viable and cost-effective means for regularization, and we show that GPU-acceleration reduces its overhead to very moderate levels.
40434292
Fusion Algorithm Based on the Intuitionistic Fuzzy Set and Multiple Neural Network
The paper proposes an algorithm based on the intuitionistic fuzzy set theory to fuse a lot of different neural network. Apply it to the comprehensive assessment of the target destruction effect in the battle field, confirm the weights of different neural networks, and synthesize their assessment results as the final outputting result according to the weight. Apply the algorithm to instance simulation, the result shows its validity and rationality.