Metric learning methods, for person re-identification, estimate a scaling for distances in a vector space that is optimized for picking out observations of the same individual. This paper presents a novel approach to the pedestrian re-identification problem that uses metric learning to improve the state-of-the-art performance on standard public datasets. Very high dimensional features are extracted from the source color image. A first processing stage performs unsupervised PCA dimensionality reduction, constrained to maintain the redundancy in color-space representation. A second stage further reduces the dimensionality, using a Local Fisher Discriminant Analysis defined by a training set. A regularization step is introduced to avoid singular matrices during this stage. The experiments conducted on three publicly available datasets confirm that the proposed method outperforms the state-of-the-art performance, including all other known metric learning methods. Further-more, the method is an effective way to process observations comprising multiple shots, and is non-iterative: the computation times are relatively modest. Finally, a novel statistic is derived to characterize the Match Characteristic: the normalized entropy reduction can be used to define the 'Proportion of Uncertainty Removed' (PUR). This measure is invariant to test set size and provides an intuitive indication of performance.

/pdf/local-fisher-discriminant-analysis-for-pedestrian-re-2vk7l3e6vk.pdf

Local Fisher Discriminant Analysis for Pedestrian Re-identification

Computer vision techniques for construction safety and health monitoring

A deep hybrid learning model to detect unsafe behavior: Integrating convolution neural networks and long short-term memory

A vision-based motion capture and recognition framework for behavior-based safety management

Wearable technology for personalized construction safety monitoring and trending: Review of applicable devices

Tracking multiple workers on construction sites using video cameras

Visual monitoring of construction worksites through the installation of surveillance cameras has become prevalent in the construction industry. These cameras also are useful for automatic observation of construction events and activities. This paper demonstrates the use of a surveillance camera for assessing tower crane activities during the course of a workday. In particular, it seeks to demonstrate that the crane jib trajectory, together with known information regarding the site plans, provides sufficient information to infer the activity states of the crane. The jib angle trajectory is tracked by using two-dimensional to three-dimensional rigid pose tracking algorithms. The site plan information includes a process model for the activities and site layout information. A probabilistic graph model for crane activity is designed to process the track signals and recognize crane activity as belonging to one of two categories: concrete pouring and nonconcrete material movement. The experimental result...

Vision-Based Tower Crane Tracking for Understanding Construction Activity

Vision-based action recognition of construction workers using dense trajectories

A performance evaluation of vision and radio frequency tracking methods for interacting workforce

Visual monitoring of construction work sites through the installation of surveillance cameras has become prevalent in the construction industry. Cameras also have practical utility for automatic observation of construction events and activities. This paper demonstrates the use of a surveillance camera for assessing tower crane activities during the course of a work day. The jib angle and the trolley position are tracked using 2D-3D rigid pose estimation and density-based tracking algorithms, respectively. A finite-state machine model for crane activity is designed to process the track signals and recognize crane activity as belonging to one of the two categories: concrete pouring and non-concrete material movement. Experimental results from a construction surveillance camera show that crane activities are correctly identified.

Zhongke Shi

Papers

Tracking multiple workers on construction sites using video cameras

Vision-Based Tower Crane Tracking for Understanding Construction Activity

Vision-based action recognition of construction workers using dense trajectories

A performance evaluation of vision and radio frequency tracking methods for interacting workforce

Vision-Based Crane Tracking for Understanding Construction Activity