Closed fbh16 closed 3 months ago
Thank you for your question and interest in our work. Regarding the impact of $\sigma_m$ on tracking performance, we have discussed this in our ablation studies. As shown in Figure 4(b), the influence of $\sigma_m$ is not particularly significant. We consider $\sigma_m$ to be a relative measure that reflects the uncertainty in the detection of target height, which is largely independent of the image resolution.
In our practical implementations, we used a value of $\sigma_m = 0.05$ and found that fine-tuning this parameter is not necessary. Its impact on the tracking performance is negligible compared to the influence of parameters like $w_x$ and $w_y$. Therefore, in scenarios with varying image resolutions, adjusting $\sigma_m$ might not yield substantial changes in the tracking results.
I hope this clarifies your doubts. If you have any further questions or need more information, please feel free to reach out.
Hi Corfyi,
I have another question I'd like to consult you about. In the paper, the Mahalanobis distance $D$ between the probability distributions of the observations and predictions is calculated to measure their similarity, thereby maintaining the target's ID at different time steps.
My question is, does a smaller $D$ indicate higher similarity between the distributions of the observations and predictions (i.e., representing the same target), or does a larger one indicate higher similarity?
The $D$ is constructed using the covariance and the mean of the distributions of observations and predictions (Eq. 6-Eq. 8 in the paper). Therefore, the larger the residual $\epsilon$ and the residual covariance $S$ between the two distributions is, the larger the $D$ become. So, I lean towards the idea that a smaller $D$ indicates higher similarity.
However, in practical parameter tuning, I found that setting a larger Mahalanobis distance threshold (cost_limit), with reference to the elements in the cost matrix, actually leads to better tracking performance. Conversely, setting a smaller threshold results in more IDsw. https://github.com/corfyi/UCMCTrack/blob/7851a49078a6666725d1bda670f0046fcc0d715f/tracker/ucmc.py#L15
Could you please share your understanding? If there are any errors in my statement, please feel free to point them out. Thanks!
Hi Corfyi, many thanks for releasing this impressive work. I have a confusion regarding the construction of the measurement noise covariance matrix R_k in the CMD (Correlated Measurement Distribution) section. Specifically, in relation to the detection noise factor(sigma_m=0.05) which is encountered within the getUVError function in the code, my understanding is that some fine-tuning may be necessary for images/videos with different resolutions. Additionally, when dealing with images or videos that have resolutions differing from the demo, I'm uncertain about how the constraints on pixel errors along the u and v axes in the getUVError function (specifically, the several hyperparameters like u>13, u<2, and v>10) should be adjusted. However, I am not entirely confident in my interpretation, and I would greatly appreciate hearing your perspective on this matter.