Multi-Object Tracking in Drone Videos

In this report, three popular methods for multi-pedestrian tracking are extended to a multi-category setting and tested on a large drone-based dataset. A thorough comparison of the algorithms is presented and a common shortcoming is identified. Building on this, a new tracking-by-detection based approach is developed that outperforms the other methods by a large margin. In addition, a state-of-the-art object detection model is adapted for the drone imagery, since no public detections are available for the dataset

Multi Person Tracking with a Multi Hypothesis Approach for Ambiguous Assignments

Multi-person tracking is often solved with the tracking-by-detection paradigm, in that a distance measure is calculated for each possible track-detection assignment. Then, the sum of distances of all assignments has to be minimized, for which mostly the Hungarian method is used. Wheareas it is easy to design a distance measure that can clearly indicate the correct assignments in sequences with sparse person distributions, the distances of some assignments can be very similar in crowded scenes, where multiple persons share similar spatial positions and appearances. As a consequence, wrong assignments are inescapable, harming the tracking performance. In contrast of executing all assignments simultaneously, no matter if they are clear or ambiguous, this work treats ambiguous assignments with similar distances separately following a multihypothesis approach, updating the hypotheses until the assignment task is clear again. To determine which assignments are considered ambiguous, a method that compares the entries in the distance matrix of track-detection assignments is introduced. No further information next to the distance matrix is needed, which makes the proposed approach applicable to any tracking-by-detection based method. Experimental results show that the separate treatment of ambiguous assignments can improve the tracking performance in crowds and thus is a promising research directory

A Detailed Study of the Association Task in Tracking-by-Detection-based Multi-Person Tracking

Many multi-person trackers follow the tracking-by-detection paradigm applying a person detector in each frame and linking detections of the same target to form tracks in the association task. While the basic concept is the same among these methods, various motion models, distance metrics to measure the similarity of targets, and matching strategies are used. This makes it difficult to compare different methods and also to assess the influence of single tracking components on the final performance. For these reasons, all parts of the association task are thoroughly investigated in this study. Starting with a simple baseline which is consequently improved with the help of experimental results, a strong tracking-by-detection-based framework is developed that achieves state-of-the-art performance on two multi-person tracking benchmarks

Inferring Chemical Reaction Patterns Using Rule Composition in Graph Grammars

Modeling molecules as undirected graphs and chemical reactions as graph rewriting operations is a natural and convenient approach tom odeling chemistry. Graph grammar rules are most naturally employed to model elementary reactions like merging, splitting, and isomerisation of molecules. It is often convenient, in particular in the analysis of larger systems, to summarize several subsequent reactions into a single composite chemical reaction. We use a generic approach for composing graph grammar rules to define a chemically useful rule compositions. We iteratively apply these rule compositions to elementary transformations in order to automatically infer complex transformation patterns. This is useful for instance to understand the net effect of complex catalytic cycles such as the Formose reaction. The automatically inferred graph grammar rule is a generic representative that also covers the overall reaction pattern of the Formose cycle, namely two carbonyl groups that can react with a bound glycolaldehyde to a second glycolaldehyde. Rule composition also can be used to study polymerization reactions as well as more complicated iterative reaction schemes. Terpenes and the polyketides, for instance, form two naturally occurring classes of compounds of utmost pharmaceutical interest that can be understood as "generalized polymers" consisting of five-carbon (isoprene) and two-carbon units, respectively

Generic Strategies for Chemical Space Exploration

Computational approaches to exploring "chemical universes", i.e., very large sets, potentially infinite sets of compounds that can be constructed by a prescribed collection of reaction mechanisms, in practice suffer from a combinatorial explosion. It quickly becomes impossible to test, for all pairs of compounds in a rapidly growing network, whether they can react with each other. More sophisticated and efficient strategies are therefore required to construct very large chemical reaction networks. Undirected labeled graphs and graph rewriting are natural models of chemical compounds and chemical reactions. Borrowing the idea of partial evaluation from functional programming, we introduce partial applications of rewrite rules. Binding substrate to rules increases the number of rules but drastically prunes the substrate sets to which it might match, resulting in dramatically reduced resource requirements. At the same time, exploration strategies can be guided, e.g. based on restrictions on the product molecules to avoid the explicit enumeration of very unlikely compounds. To this end we introduce here a generic framework for the specification of exploration strategies in graph-rewriting systems. Using key examples of complex chemical networks from sugar chemistry and the realm of metabolic networks we demonstrate the feasibility of a high-level strategy framework. The ideas presented here can not only be used for a strategy-based chemical space exploration that has close correspondence of experimental results, but are much more general. In particular, the framework can be used to emulate higher-level transformation models such as illustrated in a small puzzle game