Local Decorrelation for Improved Pedestrian Detection
Even with the advent of more sophisticated, data-hungry methods, boosted decision trees remain extraordinarily successful for fast rigid object detection, achieving top accuracy on numerous datasets. While effective, most boosted detectors use decision trees with orthogonal (single feature) splits, and the topology of the resulting decision boundary may not be well matched to the natural topology of the data. Given highly correlated data, decision trees with oblique (multiple feature) splits can be effective. Use of oblique splits, however, comes at considerable computational expense. Inspired by recent work on discriminative decorrelation of HOG features, we instead propose an efficient feature transform that removes correlations in local neighborhoods. The result is an overcomplete but locally decorrelated representation ideally suited for use with orthogonal decision trees. In fact, orthogonal trees with our locally decorrelated features outperform oblique trees trained over the original features at a fraction of the computational cost. The overall improvement in accuracy is dramatic: on the Caltech Pedestrian Dataset, we reduce false positives nearly tenfold over the previous state-of-the-art.
The paper introduces an efficient feature transform of local decorrelation, which when combined with boosted (orthogonal) decision trees, considerably improves over the state-of-the-art on pedestrian detection. Overall, it is a clearly (and nicely) written paper with good analysis, enough details and solid experiments. Pros:- Very well written and executed paper- Attention to detail- Solid results- Straight forward and intuitive methodCons:- Incremental from Hariharan et al. (not major, see later)- If it claims ``Improved Detection'', as opposed to ``Improved Pedestrian Detection'', then I would have liked to see some more results on object detection or likewise.- Some minor analysis and experiments I would live to see (see later)- Some parts can be better explained (see later)- Relevance to NIPS (see later)Some comments and concerns:Incremental nature -- Though it might seem that this paper is an incremental work, I would say that it has enough novelty to offer over [15]. Going from global to local decorrelation, and doing the right analysis for design decisions set it apart. Though the analysis are in line with [6], I don't see that as a negative; I rather welcome the analysis.Detection vs. Pedestrian Detection -- I would suggest the title to be changed to reflect that this work is currently targeted towards Pedestrian detection. Otherwise, I would like to see some experiments (may be similar to [15], or some work which uses Channel features) to show the effectiveness on standard detection tasks. Just to be clear: I am not asking for more state-of-the-art results.Experiments/analysis --1. L289-92: How does downsampling the final set of feature impact the overall performance? Does it improve quantitative performance, or just improves efficiency at the cost some quantitative performance?2. Table 2 (6. LDCF constant): This is a very interesting result for me. Using k DCT basis filters itself takes them from 17.3% to 14.2% MR. Can the authors perform more analysis on using these DCT basis? I'm thinking on the lines of Fig. 4 for DCT basis. Does varying the DCT basis or the size of the filters improves the performance further? What's the impact of using more/less tree depth and sampling interval? I think this analysis will be interesting, and will give a new perspective to using these DCT basis. However, I don't think higher performance from these constant filters undermines the impact/novelty of this paper.3. L375-77 (Fig. 4c): I see that the performance is still improving with decreasing tree depth and sampling interval. Did the authors try to push it more? Does the performance plateau? It would be nice to see that. If it wasn't feasible to push further, it can be mentioned.4. "Replacing oblique splits": I'm not in favor of stating that the current method can act as a replacement of oblique splits. To state that, I would like to see both are ultimately doing the same thing (may be by analyzing the hits/misses at test time etc.) But this is a personal preference.Clarity --1. Section 4 (Complexity): How are #patches=d? Use of \Sigma and \bar\Sigma etc. I think this part can be made clearer.2. Section 5 (observations): I think it might be a good idea to ref. each observation to a section/sub-section to make the obvious connection.3. (minor) L269: How many levels per octave in feature pyramid? Given that authors are releasing code, this might not be necessary.4. Section 5 (Estimating \Sigma): Which large collection of images did the authors use? #images and #patches?5. Caltech Results (L409-10): ACF-Caltech is the standard ACF paper trained on Caltech? ACF-Caltech+ is just using more training data in a more complex model? By the ``ability to effectively utilize ..", do the authors mean computational ability or something else? Some clarity in this part will help!6. Fig. 5: Having citations with each result in legends might be a good idea. If authors are not citing all paper due to space limitation, partial citation list will also be helpful.Relevance -- I think this paper might be slightly better suited for a vision venue as opposed to NIPS. But I definitely see merit and novelty in the paper, and its potential impact. I would argue for the paper!