Physics-based Person Tracking Using the Anthropomorphic Walker

Contribution:
Evaluation:
Reproducibility:
Improvements:

Contribution : Physics based Model for tracking people,as an alternative to other methods, for instance kinematic models, is suggested. Instead of modeling full body dynamics, a biomechanics based lower body tracking model is implemented.

Evaluation: The model was evaluated for 4 different types of walking and environmental variations. Three of these were generated specifically to test the model performance in changes in speed, simulated occlusion and turning. The model then was applied to an external dataset (HumanEva).

Reproducibility: The components of the model have been explained considerably well, though lots of the have been referred to previous work and weren't revisited. THe parameters of most of these components appeared to have been set manually or empirically while evaluation. So, there might stay some room for ambiguity while reproducing the results completely.

Improvements: Overall the paper was well written and explained. Model parameter selection could have been emphasized more.

-- Main.sumanm - 01 Dec 2011

Contribution: A new method for tracking is proposed, where the underlying model is not kinematic but rather physics-based. The method function without learning and is real time, but focuses mainly on walking, and the tracking is based on monocular video sequences.

Evaluation: I think this part was actually very good. The showed a very good visualization of their results vs reality by an overlaid image, and the carried out a variety of experiments (various speeds, etc) to show robustness. Some numerical values would have been perfection, but those are in general difficult to obtain.

Improvements: performance would probably be boosted by using more than one point of view. As it is, it seems they can only track movements that lie perfectly on a plane perpendicular to the direction of the camera, which while a good first step, is far from optimal.

Reproducibility: I think this is the part where the paper is most laking. While explicit formulas and algorithms are given and spelled out, parameters are not specified, nor are the conditions of the recording and so on. This paper is fairly complex and hard to describe with perfect accuracy, however, so the authors had to compromise between length and precision.

-- Main.ginestra - 01 Dec 2011

Contribution: The paper presents a method for tracking the locomotion of a human from single-camera video sequences. This is accomplished by first contructing a 2-dimensional physics-based model of lower-body locomotion called an Anthropomorphic Walker. This is used to form a generative model of a simplified 3-dimensional kinematic human body. These two components are then used along with a likelihood function that describes the relationship between poses and images captured in the video. Through Bayesian inference, the most likely pose at each frame is computed.

Evaluation: The system is evaluated in four different experiments. The first three experiments use monocular video data of different nontrivial locomotion scenarios: variable speed walking, occlusion (walking behind obstacles), and turning out of the camera plane. The fourth experiment involves testing on a pre-existing benchmark dataset. The fourth experiment allows for explicit quantitative analysis of the accuracy of the results.

Reproducible: I believe this paper would be very difficult to reproduce. Many details of the implementation are left out or incomplete, such as the methods used to extract data from the video frames. It shouldn't be impossible, however, since most of the gaps can likely be filled in with other papers and tools from related fields like computer vision, inverse kinematics and Bayesian inference. By itself, though, I don't think this paper provides sufficient detail to reproduce its results.

Improvement: The paper is well-organized and the results are presented in a way that's easy to understand and appreciate. I found the formulas to be confusing however, usually because it wasn't entirely obvious what each symbol being used meant. Referring back to the reproducibility of the paper, I also feel the paper would benefit from additional detail on some of the algorithms and tools used to implement their system.

-- Main.cdoran - 01 Dec 2011

Contribution:
The study proposes a physics-based model for 3D tracking of a person from monocular video sequences. The presented model applies a probabilistic approach and it exploits basic physical principles in the design of a prior density model over human motion. It also uses a low-dimensional, biomechanically inspired models. As a result, it achieves crucial characteristics of bipedal locomotion such as balance and ground contact. Moreover, the model aims for reducing the dependence of models on mocap data which in turn enable models to generalize to different styles, such as varying walking speed, step length, and mass, as well as avoiding some of the problems of other approaches, e.g. footstake.

Evaluation:
The study includes four experiments for evaluating the accuracy and the generalization capabilities of the proposed model. Experiment 1 and 3 demonstrates the genralization of the model across different walking speeds, and motions styles, such as turning. Experiment 2 shows the robustness of the method as it can still track a person even when there are occlusions. Lastly, fourth experiment supports the results by using the HumanEva dataset, as it quantitatively compares and assesses the tracking results with the ground truth. It turns out that estimating the depth information is, unsurprisingly, the hardest part of such a problem.

Reproducibility:
Even though the theoretical explanations are detailed out, most of the implementation details, as well as the hand-tuned parameters are left out from the paper. One has to look at the given reference papers for the implementation. On the other hand, they have released a version of their implementation, which would definitely be helpful to reproduce the results.

Improvements:
A possible improvement for that study would be to investigate possible controllers for generalizing over different morphologies, or even moods of characters. The paper is well-written, but it's not very easy to follow as it incorporates ideas/methods from different areas.

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Contribution: The paper presents a physics-based model to track walking people in monocular video while most current methods rely on kinematic models.

Evaluation: The result is evaluated by 4 experiments. The first one tests the changes in speed from 7 to 3 km/h. The second one tests occlusion by blacking out an image region. Third one tests the track of 3D turning. The last one tests against humanEva dataset.

Reproducibility: This paper presents a complex topic. While the key ideas, formulas, and general ideas of experiments are presented with details, the actual implementation still misses a lot of explanation. It may not be easy to reproduce exactly what the paper does.

Improvement: Overall the paper is well organized and presents the key ideas very well. However I still find it is hard to follow because it requires so much background knowledge from different areas.

Baoxuan- 01 Dec 2011

-- Main.ooguz - 01 Dec 2011

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** Contribution? It uses physics-based model that generalizes naturally to variations in style of different speed, step-length, and mass, and avoids common problems such as footskate.

** How are the results evaluated? They evaluated the results by doing four experiments: change in speed, occlusion, turning and using the HumanEva mocap data. To measure the quality of tracking, they plot the spped of subject along with the posterior probability of which leg is the stance leg. They also watch the 3D animation to see how realistic the walking motion is.

** Reproducible? Looks to be reproducible. The methods and equations are well documented and values of parameters are also given.

** Possible improvements? This paper crosses many different fields. There are many concepts that are not explained clearly to me. It feels very difficult to read this paper. Otherwise, it's a well written paper except figure 8 comes after figure 11.

-- Main.shuoshen - 01 Dec 2011

-- MichielVanDePanne - 27 Nov 2011