Our dataset contains sequences with two and three motions. The sequences can be roughly divided into three categories:

• Checkerboard sequences: this category consists of 104 sequences of indoor scenes taken with a handheld camera under controlled conditions. The checkerboard pattern on the objects is used to assure a large number of tracked points.
• Traffic sequences: this category consists of 38 sequences of outdoor traffic scenes taken by a moving handheld camera.
• Other (Articulated/non-rigid sequences): this category contains 13 sequences displaying motions constrained by joints, head and face motions, people walking, etc.

The following table presents some information about the dataset: the number of sequences, the average number of tracked features and the average number of frames for each one of the categories aformentioned, distinguishing also between sequences with two and three motions.

	2 Motions			3 Motions
	# Seq.	Points	Frames	# Seq.	Points	Frames
Checkerboard	78	291	28	26	437	28
Traffic	31	241	30	7	332	31
Others	11	155	40	2	122	31
All	120	266	30	35	398	29

Some samples from the Hopkins 155 dataset

In the table below, we summarize the results obtained by the all the algorithms that we have tested on this dataset. We report the average misclassification error for the sequences with two and three motions. We compare algorithms based on algebraic, statistical and others principles. For more details on the algoritms, please visit the motions segentation research webpage.

Checker.	GPCA	LSA 5	LSA 4n	MSL	RANSAC	DI-GPCA	DI-LSA	SI-GPCA	SI-LSA	LLMC 5	LLMC 4n	CCS	ALC 5	ALC 4n	SSC
Average	6.09%	8.84%	2.57%	4.46%	6.52%	4.29%	2.86%	4.18%	2.61%	4.37%	4.65%	16.37%	2.56%	1.49%	1.12%
Median	1.03%	3.43%	0.27%	0.00%	1.75%	2.09%	0.25%	2.24%	0.42%	0.00%	0.11%	10.64%	0.00%	0.27%	0.00%
Traffic	GPCA	LSA 5	LSA 4n	MSL	RANSAC	DI-GPCA	DI-LSA	SI-GPCA	SI-LSA	LLMC 5	LLMC 4n	CCS	ALC 5	ALC 4n	SSC
Average	1.41%	2.15%	5.43%	2.23%	2.55%	0.52%	5.74%	0.65%	4.54%	0.84%	3.65%	5.27%	2.83%	1.75%	0.02%
Median	0.00%	1.00%	1.48%	0.00%	0.21%	0.00%	1.55%	0.00%	1.30%	0.00%	0.33%	0.00%	0.30%	1.51%	0.00%
Other	GPCA	LSA 5	LSA 4n	MSL	RANSAC	DI-GPCA	DI-LSA	SI-GPCA	SI-LSA	LLMC 5	LLMC 4n	CCS	ALC 5	ALC 4n	SSC
Average	2.88%	4.66%	4.10%	7.23%	7.25%	4.79%	7.95%	4.55%	4.38%	6.16%	5.23%	17.58%	6.90%	10.70%	0.62%
Median	0.00%	1.28%	1.22%	0.00%	2.64%	0.43%	1.39%	0.43%	1.39%	1.37%	1.30%	7.07%	0.89%	0.95%	0.00%
All	GPCA	LSA 5	LSA 4n	MSL	RANSAC	DI-GPCA	DI-LSA	SI-GPCA	SI-LSA	LLMC 5	LLMC 4n	CCS	ALC 5	ALC 4n	SSC
Average	4.59%	6.73%	3.45%	4.14%	5.56%	3.36%	4.07%	3.30%	3.27%	3.62%	4.44%	12.16%	3.03%	2.40%	0.82%
Median	0.38%	1.99%	0.59%	0.00%	1.18%	0.59%	0.51%	0.53%	0.53%	0.00%	0.24%	0.00%	0.00%	0.43%	0.00%

Histograms and cumulative distributions of the errors per sequence

Checker.	GPCA	LSA 5	LSA 4n	MSL	RANSAC	DI-GPCA	DI-LSA	SI-GPCA	SI-LSA	LLMC 5	LLMC 4n	CCS	ALC 5	ALC sp	SSC
Average	31.95%	30.37%	5.80%	10.38%	25.78%	20.54%	6.67%	22.44%	5.55%	10.70%	12.01%	28.63%	6.78%	5.00%	2.97%
Median	32.93%	31.98%	1.77%	4.61%	26.01%	17.30%	1.00%	23.20%	1.21%	9.21%	9.22%	33.21%	0.92%	0.66%	0.27%
Traffic	GPCA	LSA 5	LSA 4n	MSL	RANSAC	DI-GPCA	DI-LSA	SI-GPCA	SI-LSA	LLMC 5	LLMC 4n	CCS	ALC 5	ALC sp	SSC
Average	19.83%	27.02%	25.07%	1.80%	12.83%	2.46%	10.21%	8.00%	9.51%	2.91%	7.79%	3.02%	4.01%	8.86%	0.58%
Median	19.55%	34.01%	23.79%	0.00%	11.45%	0.55%	4.71%	2.06%	4.71%	0.00%	5.47%	0.18%	1.35%	0.51%	0.00%
Other	GPCA	LSA 5	LSA 4n	MSL	RANSAC	DI-GPCA	DI-LSA	SI-GPCA	SI-LSA	LLMC 5	LLMC 4n	CCS	ALC 5	ALC sp	SSC
Average	16.85%	23.11%	7.25%	2.71%	21.38%	6.72%	2.13%	7.05%	3.52%	5.60%	9.38%	44.89%	7.25%	21.08%	1.42%
Median	28.66%	23.11%	7.25%	2.71%	21.38%	6.72%	2.13%	7.05%	3.52%	5.60%	9.38%	44.89%	7.25%	21.08%	0.00%
All	GPCA	LSA 5	LSA 4n	MSL	RANSAC	DI-GPCA	DI-LSA	SI-GPCA	SI-LSA	LLMC 5	LLMC 4n	CCS	ALC 5	ALC sp	SSC
Average	28.66%	29.28%	9.73%	8.23%	22.94%	18.38%	10.89%	17.50%	9.77%	8.85%	11.02%	26.18%	6.26%	6.69%	2.45%
Median	28.26%	31.63%	2.33%	1.76%	22.03%	16.11%	4.04%	16.74%	2.33%	3.19%	6.81%	31.74%	1.02%	0.67%	0.20%

Histograms and cumulative distributions of the errors per sequence

GPCA	Generalized PCA
LSA 5	Local Subspace Analysis (projection to a space of dimension 5)
LSA 4n	Local Subspace Analysis (projection to a space of dimension 4 times the number of motions)
MSL	Multi Stage Learning
RANSAC	RANdom SAmple Consensus
DI-GPCA	Iterative Perspective Algorithm, Depth-Initialization, Subspace separation using GPCA
DI-LSA	Iterative Perspective Algorithm, Depth-Initialization, Subspace separation using LSA
SI-GPCA	Iterative Perspective Algorithm, Segmentation-Initialization, Subspace separation using GPCA
SI-LSA	Iterative Perspective Algorithm, Segmentation-Initialization, Subspace separation using LSA
LLMC 5	Local Linear Manifold Clustering (projection to a space of dimension 5)
LLMC 4n	Local Linear Manifold Clustering (projection to a space of dimension 4 times the number of motions)
CCS	Connected Component Search
ALC 5	Agglomerative Subspace Clustering (projection to a space of dimension 5)
ALC 4n	Agglomerative Subspace Clustering (projection to a space of dimension 4 times the number of motions)
SSC	Sparse Subspace Clustering

To download the dataset and our implementations of GPCA, RANSAC and LSA, please follow the link below (registration required).

Hopkins 155 + Code Download

Code is available online for some of the motion segmentation algorithms.

• Robust Motion Segmentation via Lossy Compression (ALC): http://perception.csl.illinois.edu/coding/motion/
• Improved Multistage Learning for Multibody Motion Segmentation (Improved version of MSL): http://www.iim.ics.tut.ac.jp/~sugaya/public-e.html