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Inthis paperwe presentan approach foroptical vehicle positioning (e.g. fork lifters). Our approach is motivated by planar marker detection systems like ARTag or ARToolkit, in which poses of planarmarkers relative to the camera can be determined. In contrast toexisting optical positioning systems(e.g. SkyTrax), we mount cameras on the ceiling and passive (non-electronic) planar markers on the top of the vehicles. The absenceof complexelectronic components on the high stressed vehicles enablesrapid process integration, which is particularly important forrental vehicles. We have evaluated our method keeping the most important user requirementscoverage, costsand accuracyunder consideration for three intra-logistic scenarios a) zone monitoring with zone precise positioning, b) storage aisle monitoring with storage place precise positioning and, c) complete driving rangemonitoringwith maximumprecision.

The governing behaviors of individuals in crowded places offer unique and difficult challenges, and limit the scope of conventional surveillance systems. In this paper, we investigate the crowd behaviors and localize the anomalies due to individual's abrupt dissipation. The novelty of the proposed approach can be described in three aspects. First, we introduce block-clips by sectioning the video segments into non-overlapping spatio-temporal patches to marginalize the arbitrarily complicated and dense flow field. Second, we treat the flow field in each block-clip as 2d distribution of samples and mixtures of Gaussian is used to parameterize it keeping generality of flow field intact. K-means algorithm is employed to initialize the mixture model and is followed by Expectation Maximization for optimization. These mixtures of Gaussian result in the distinct flow patterns precisely a sequence of dynamic patterns for each block-clip. Third, a bank of Conditional Random Field model is employed one for each block clip and is learned from the sequence of dynamic patterns and classifies each block-clip as normal and abnormal. We conduct experiment on two challenging benchmark crowd datasets PETS 2009 and University of Minnesota and results show that our method achieves higher recognition rates in detecting specific and overall crowd behaviors. In addition, the proposed approach shows dominating performance during the comparative analysis with similar approaches in crowd behavior detection.

A generic approach for tracking humans and objects under occlusion using semantic analysis is presented. The aim is to exploit knowledge representation schemes, precisely semantic logic where each detected object is represented by a node and the association among the nodes is interpretated as flow paths. Besides, maximum likelihood is computed using our CWHI technique and Bhattacharyya coefficient. These likelihood weights are mapped onto the semantic network to efficiently infer the multiple possibilities of tracking by the manipulation of “propositional logic” at a time window. The logical propositions are built by formularizing facts, semantic rules and constraints associated with tracking. Currently, we are able to handle tracking under normal, occlusion, and split conditions. The experimental results show that the proposed approach enables accurate and reliable tracking by resolving the ambiguities of online data association under occlusions.

This paper proposes a system to recognize isolated American Sign Language and Arabic numbers in real-time from stereo color image sequences using Hidden Markov Models (HMMs). Our system is based on three main stages; preprocessing, feature extraction and classification. In preprocessing stage, color and 3D depth map are used to detect and track the hand. The second stage, 3D combined features of location, orientation and velocity with respected to Cartesian and Polar systems are used. Additionally, k-means clustering is employed for HMMs code-word. In the final stage, the hand gesture path is recognized using Left-Right Banded topology (LRB) in conjunction Viterbi path. Experimental results demonstrate that, our system can successfully recognize isolated hand gestures with 98.33% recognition rate.

Kalman filtering, a recursive state estimation filter is a robust method for tracking objects. It has been proven that Kalman filter gives a good estimation when tested on various tracking systems. However, unsatisfying tracking results may be produced due to different real-time conditions. These conditions include: inter-object occulusion and separation which are observed when objects are being tracked in real-time. Thus, it is challenging to handle for the classical Kalman filter. In this paper, we proposed an idea of intelligent feature-guided tracking using Kalman filtering. A new method is developed named Correlation-Weighted Histogram Intersection (CWHI), in which correlation weights are applied to Histogram Intersection (HI) method. We focus on multi-object tracking in traffic sequences and our aim is to achieve efficient tracking of multiple moving objects under the confusing situations. The proposed algorithm achieves robust tracking with 97.3% accuracy and 0.07% covariance error in different real-time scenarios.