Fusion of Head and Full-Body Detectors for Multi-Object Tracking

In order to track all persons in a scene, the tracking-by-detection paradigm has proven to be a very effective approach. Yet, relying solely on a single detector is also a major limitation, as useful image information might be ignored. Consequently, this work demonstrates how to fuse two detectors into a tracking system. To obtain the trajectories, we propose to formulate tracking as a weighted graph labeling problem, resulting in a binary quadratic program. As such problems are NP-hard, the solution can only be approximated. Based on the Frank-Wolfe algorithm, we present a new solver that is crucial to handle such difficult problems. Evaluation on pedestrian tracking is provided for multiple scenarios, showing superior results over single detector tracking and standard QP-solvers. Finally, our tracker ranks 2nd on the MOT16 benchmark and 1st on the new MOT17 benchmark, outperforming over 90 trackers.Comment: 10 pages, 4 figures; Winner of the MOT17 challenge; CVPRW 201

The linearization problem of a binary quadratic problem and its applications

We provide several applications of the linearization problem of a binary quadratic problem. We propose a new lower bounding strategy, called the linearization-based scheme, that is based on a simple certificate for a quadratic function to be non-negative on the feasible set. Each linearization-based bound requires a set of linearizable matrices as an input. We prove that the Generalized Gilmore-Lawler bounding scheme for binary quadratic problems provides linearization-based bounds. Moreover, we show that the bound obtained from the first level reformulation linearization technique is also a type of linearization-based bound, which enables us to provide a comparison among mentioned bounds. However, the strongest linearization-based bound is the one that uses the full characterization of the set of linearizable matrices. Finally, we present a polynomial-time algorithm for the linearization problem of the quadratic shortest path problem on directed acyclic graphs. Our algorithm gives a complete characterization of the set of linearizable matrices for the quadratic shortest path problem

Using a conic bundle method to accelerate both phases of a quadratic convex reformulation

We present algorithm MIQCR-CB that is an advancement of method MIQCR~(Billionnet, Elloumi and Lambert, 2012). MIQCR is a method for solving mixed-integer quadratic programs and works in two phases: the first phase determines an equivalent quadratic formulation with a convex objective function by solving a semidefinite problem $(SDP)$, and, in the second phase, the equivalent formulation is solved by a standard solver. As the reformulation relies on the solution of a large-scale semidefinite program, it is not tractable by existing semidefinite solvers, already for medium sized problems. To surmount this difficulty, we present in MIQCR-CB a subgradient algorithm within a Lagrangian duality framework for solving $(SDP)$ that substantially speeds up the first phase. Moreover, this algorithm leads to a reformulated problem of smaller size than the one obtained by the original MIQCR method which results in a shorter time for solving the second phase. We present extensive computational results to show the efficiency of our algorithm

Eine Heuristik für quadratische 0-1-Probleme

Eine neue Modellformulierung für kombinatorische Optimierungsprobleme wird vorgestellt. Auf der Grundlage des quadratischen 0-1-Problems ohne Nebenbedingungen wird ein vorteilhafter Typ kombinatorischer Optimierungsprobleme eingeführt. Eine einfache Heuristik, die sog. RII-Methode (randomized iterative improvement), erlaubt es, diesen Modelltyp effizient zu lösen. Die Flexibilität des Ansatzes wird an der Modellierung des K-Färbungsproblems gezeigt. Es werden Rechenergebnisse für Färbungsprobleme aus der Literatur präsentiert. Wendet man Randomized Iterative Improvement auf den neuen Modelltyp an, können diese Testprobleme effizient gelöst werden.A new model formulation for combinatorial optimization problems is presented. Based on the unconstrained quadratic program (binary quadratic program), a favourable type of combinatorial optimization problems is introduced. A simple heuristic method, i.e. randomized iterative improvement (RII), permits to solve this type of model efficiently. Modelling of the K-colouring problem shows the flexibility of the approach. Computational results on data sets from the literature about vertex colouring are reported. These benchmark problems are shown to be solved efficiently using randomized iterative improvement and the new type of model

Survey on Heuristic Search Techniques to Solve Artificial Intelligence Problems

Artificial intelligence (AI) is an area of computer science that highlights the creation of machines that are intelligent, also they work and react like humans. Since AI problems are complex and cannot be solved with direct techniques we resort to heuristic search techniques. Heuristic search technique is any approach to problem solving, learning, or discovery which uses a practical methodology which is not guaranteed to be optimal or perfect, but it is sufficient for the immediate goals. This paper surveys some of the heuristic techniques which is used for searching an optimal solution among multiprocessor environment, followed by and method which enhances the search by doing a search in bidirection and also a method for task scheduling in multiprocessor environment. The paper also discuses about how heuristic is used to solve binary quadratic program and also how it is used in 3G (3rd Generation) Universal Mobile Telecommunication System (UMTS) network. DOI: 10.17762/ijritcc2321-8169.15058

Particle algorithms for optimization on binary spaces

We discuss a unified approach to stochastic optimization of pseudo-Boolean objective functions based on particle methods, including the cross-entropy method and simulated annealing as special cases. We point out the need for auxiliary sampling distributions, that is parametric families on binary spaces, which are able to reproduce complex dependency structures, and illustrate their usefulness in our numerical experiments. We provide numerical evidence that particle-driven optimization algorithms based on parametric families yield superior results on strongly multi-modal optimization problems while local search heuristics outperform them on easier problems