VAN GEMERT, JAIN, GATI, SNOEK: ACTION LOCALIZATION PROPOSALS FROM DENSE TRAJECTORIES

1

APT: Action localization Proposals
from dense Trajectories
Jan C. van Gemert1,2

1Intelligent Systems Lab Amsterdam
University of Amsterdam
Amsterdam, The Netherlands

2Computer Vision Lab
Delft University of Technology
Delft, The Netherlands

3

Qualcomm Research Netherlands
Amsterdam, The Netherlands

j.c.vangemert@gmail.com

Mihir Jain1
m.Jain@uva.nl
1

Ella Gati

ellagati@gmail.com

Cees G. M. Snoek1,3
cgmsnoek@uva.nl

Abstract
This paper is on action localization in video with the aid of spatio-temporal proposals. To
alleviate the computational expensive segmentation step of existing proposals, we propose bypassing the segmentations completely by generating proposals directly from thedense trajectories used to represent videos during classification. Our Action localization
Proposals from dense Trajectories (APT) use an efficient proposal generation algorithm
to handle the high number of trajectories in a video. Our spatio-temporal proposals are
faster than current methods and outperform the localization and classification accuracyof current proposals on the UCF Sports, UCF 101, and MSR-II video datasets.
Corrected version: we fixed a mistake in our UCF-101 ground truth. Numbers are different; conclusions are unchanged.

1

Introduction

The goal of this paper is to arrive at localization of actions in video that is faster andbetter than alternatives. Recent action localizations [9, 17, 30, 33] are inspired by successful object-proposals in static images [1, 15, 25]. Compared to sliding-subvolume approaches [10, 19, 22], proposals for action localization reduce the video search space to asmall set of spatio-temporal tubes, with high likelihood to contain an action. Different fromthese existing approaches, which all use different representations for localization and classification, we start from the observation that we can perform both tasks with one and the same
representation.
Diving deeper into two popular proposal algorithms [9, 17], three common steps are
identified: 1. Pre-processing the video by segmentation, 2. Generating tube proposals bygrouping segments and 3. Representing each tube with dense trajectory features [27, 28] so
it can be fed to an action recognition classifier. The video segmentation (step 1) easily takes
several minutes for a modest video of 400x720 pixels of 55 frames [17, 32] and can takedays for a realistic full HD movie. Moreover, a unique meaningful pixel grouping is a more
complex problem than necessarily required for proposal generation. The segmentation stepfor action proposals is overly complex and not practical for large video sets.
c 2015. The copyright of this document resides with its authors.
It may be distributed unchanged freely in print or electronic forms.

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VAN GEMERT, JAIN, GATI, SNOEK: ACTION LOCALIZATION PROPOSALS FROM DENSE TRAJECTORIES

Figure 1: We propose APT:an efficient and effective spatiotemporal proposal algorithm foraction localization.
Different
from existing work, which consider different representations forthe localization and classification
stage, we aptly re-use the dense
trajectories as used in the classification representation for localization as well. This allows largescale deployment with good localization and classification accuracy. For the action Diving
our blue best proposal results inan overlap of 0.62 with the redground truth.

We propose to skip the video segmentation of step 1 altogether. Instead, we apply the
proposal generation of step 2 on the dense trajectories of step 3. Since the dense trajectories
are computed for the representation anyway, we propose to re-use them for proposal generation as well. By founding the proposals on the same representation used for classification,
we expect the proven power of the dense trajectories [27, 28] to benefit the proposal generation. For our Action localization Proposals from dense Trajectories (APT) we cast the strong
points of current proposal methods in an efficient clustering algorithm inspired by trajectory
clustering for video segmentation, such as the seminal work of Brox & Malik [2].
We have the following contributions. i) We base our proposals on the same dense trajectories used for classification; ii) we present an efficient clustering algorithm to deal with large
numbers of trajectories; iii) we demonstrate that video segmentation is time consuming andthat its goals do not necessarily align with the goals of localization proposals. Experiments
show that APT is faster than current methods and outperforms the localization and classification accuracy of the state-of-the-art on the UCF sports, MSR-II and UCF101 datasets. In
Fig 1 we illustrate our action localization proposals.

2

Related work

Several action localization methods apply an action classifier directly on the video. Examples
include sliding 3D subvolume methods like spatio-temporal template matching [19], a 3D
boosting cascade [10] and spatio-temporal deformable 3D parts [22]. Others maximize atemporal classification path of 2D boxes through static frames [7, 23, 24, 30, 33] or search
for the optimal classification result with a branch and bound scheme [34]. The benefit is thatthese methods do not require an intermediate representation and directly apply a classifier tosampled parts of the video. The disadvantage, however, is that they have to repeat the sameprocedure for each individual action class separately which is impractical for larger numbersof action classes. Instead, we use unsupervised spatio-temporal proposals to first generate asmall set of bounding-box tubes that are likely to contain any type of action.

VAN GEMERT, JAIN, GATI, SNOEK: ACTION LOCALIZATION PROPOSALS FROM DENSE TRAJECTORIES

3

Current spatio-temporal proposals are inspired by 2D object proposals in static images.
A version of objectness [1] is extended to video [26], selective search [25] led to tubelets
from motion [9] and randomized Prim [15] was generalized to a spatio-temporal variant [17].
Several 2D object proposal methods and their 3D generalizations are based on a super-pixel
segmentation pre-processing step [1, 9, 12, 15, 17, 25, 26] which we argue is too complex a problem for proposal generation. The use of segmentation makes proposal methods
computationally too demanding for large scale video processing. To avoid the complex
pre-processing step altogether, we propose a method of generating proposals without any
pre-processing. We generate proposals from local dense trajectory features. These exact
trajectory features are re-used later on for building a representation for action classification.
Local features provide a solid base for video segmentation [2], action recognition [13, 27]
and action localization [9, 33]. Points are sampled at salient spatio-temporal locations [6,13], densely [20, 31] or along dense trajectories [27, 28]. The points are represented bypowerful local descriptors [4, 11, 14] that are robust to modest changes in motion and inappearance. Robustness to camera motion is either directly modeled from the video [8, 27]
or dealt with at the feature level by the MBH descriptor [4, 28]. After aggregating local
descriptors in a global video representation such as VLAD [8] or Fisher [16, 18, 27] they
are input to a classifier like SVM. Due to the excellent performance of dense trajectoryfeatures [27, 28] in action localization [9, 33], we adopt them as our feature representation.

3

Action Proposals from dense Trajectories

3.1

Can we use current approaches?

It may be possible to use current spatio-temporal action proposal methods on the dense trajectories. We analyze the tubelets from Jain et al. [9] and prim3D from Oneata et al. [17].
The first step of both methods is a video segmentation, assigning a single segment ID to eachpixel in the video. The second step is grouping of segments into action-proposals. Both algorithms compute feature similarities between all segment pairs that have neighboring spatiotemporal pixels. The tubelets use a greedy hierarchical clustering approach: merge the pairof most similar segments into a single segment; compute the features of this new segment
and the pairwise similarities with its spatio-temporal neighbors; repeat till convergence. The
prim3D approach applies a classifier on each segment pair to compute the likelihood of agood merge and then uses a randomized hierarchical clustering method: stochastically sample a classifier score; add segments to the hierarchy; repeat till convergence. The third stepis the actual classification of the spatio-temporal proposals. Dense trajectories are computedfor the video and each spatio-temporal proposal encodes its features into a representation fed
to an action classifier.
A strong point of tubelets is that the hierarchical clustering is unsupervised, which alleviates the need for any training data. An advantage of prim3D is that it does not need torecompute similarities after every merge. For n elements, the time complexity of prim3D
is O(n2 ), whereas recomputing similarities has a worst-case time complexity of O(n3 ). Regarding space complexity, both tubelets and prim3D have a O(n2 ) worst case complexity.
Even a time efficient implementation for prim3D in a max-heap [15], in the worst case, will
have to store n(n − 1)/2 = O(n2 ) similarities. Unfortunately, more realistic videos of over
30 seconds may easily contain three hundred thousand trajectories, which would require an
impractical 180GB of memory. Hence, we propose a new method, building on the strong
points of tubelets and prim3D.

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VAN GEMERT, JAIN, GATI, SNOEK: ACTION LOCALIZATION PROPOSALS FROM DENSE TRAJECTORIES

Property
Unsupervised
Segmentation freeTime complexity
Space complexity

Oneata et al., ECCV 2014 [17]Jain et al., CVPR 2014 [9]APT (ours)

O(n2 )O(n2 )O(n3 )O(n2 )O(n2 )O(n)Table 1: Properties of recent spatio-temporal proposal algorithms [9, 17]. APT is designed to include the strong points of existing algorithms: unsupervised, no additional segmentation step, and efficient in time and space.

3.2

Proposals from trajectories with localized SLINK

Because existing spatio-temporal proposal methods are not suitable for
Data: distance d, trajectory neighbors NN
Result: clusters in pointer representation {Π, Λ}dense trajectories, we need anotherΠ[1] = 1; Λ[1..N] = ∞; M[1..N] = ∞ ;method that can adopt the strongest
for i ← 2 to N do
points of tubelets [9] and prim3D [17].
Π[i] = i; Λ[i] = ∞; c = 1;
ids = NN[i]; M[ids]=d[i,ids];
We base our method on the SLINK alwhile c ≤ |ids| dogorithm [21] which iteratively merges
j = ids[c], q = Π[ j];the best pair of similarities in a singleif Λ[ j] ≥ M[ j] thenM[Π[ j]] = min(M[Π[ j]], Λ[ j]);link hierarchal clustering.
SLINKΛ[ j] = M[ j]; Π[ j] = i;is unsupervised, does not require reelse
computing similarities, has O(n2 ) timeM[Π[ j]] = min(M[Π[ j]], M[ j]);end
complexity and crucially only O(n)if q ∈/ ids thenspace complexity. This adopts all beninsert q in sorted ids;
efits from tubelets and prim3D, whileend
M[ j] = ∞; c = c + 1;
avoiding their impractical properties.
end
We summarize the algorithmic properfor j ← 1 to i doties of these methods in Table 1.
if Λ[ j] ≥ Λ[Π[ j]] then
Π[ j] = i;
The similarities that seed the proend
posal generation are based on imend
proved dense trajectory features [27].
end
Algorithm 1: Localized Slink.
The features cover appearance with
HOG, trajectory shape with TRAJ, motion with HOF, and change in motion with MBH. In addition, we also use the spatio-temporal
position SPAT. A combination of multiple features by multiplying similarity scores allows
capturing action type variation [9, 17], where more variation improves recall. We experimentally evaluate individual features and their normalizations.
The SLINK algorithm uses a ‘pointer representation’ to index clusters, see [21] for details. The elegance of this representation is that it can recursively be updated, allowing asingle pass through all neighboring similarities. The pointer representations consists of twoarrays Π and Λ of size n, where n is the number of trajectories. A cluster is represented byits member with the highest index i, Π[i] is then the cluster (i.e. highest index) that point i
joins, and Λ[i] is the similarity between i and cluster Π[i] when they join. The pointer representation describes a hierarchical clustering tree with n clusters. We cut the tree to output
proposals when the difference between two merging clusters is larger than 50 trajectories.
This value was found in practice to give a fair balance between the size of a cluster and thenumber of clusters.

VAN GEMERT, JAIN, GATI, SNOEK: ACTION LOCALIZATION PROPOSALS FROM DENSE TRAJECTORIES

5

Figure 2: Success and failure case of APT. In the left video our method (blue tube) ignores
the standing person (red tube) and tracks the moving actor. In the right video, there is ample
variation in depth and position yet APT tracks the action well. Our method is intended foractions and thus works best when motion is present. See Fig 1 for another successful action
localization result.
To enforce spatio-temporal compactness of the proposals we only consider the k spatiotemporal neighbors of each trajectory. We use efficient approximate k-best NN search with
the Yael library [5]. The original SLINK algorithm, however, takes similarities between all
points into account. We increase the efficiency of the original SLINK algorithm by using asorted list of point neighbors, which we can search in O(log(k)). Note that the value k in
the nearest neighbor search is a lower bound on the number of neighbors per point since thebest k neighbors is not a symmetric relation. We will experimentally evaluate various valuesof k, and the relation between the temporal axis and the spatial axis for the spatio-temporalneighbors. Pseudo code for our modified version of SLINK is given in Algorithm 1 and wewill make python code available.

4

Experiments

4.1

Datasets and evaluation

We evaluate on three diverse datasets for action localization: UCF Sports, UCF 101 andMSR-II. UCF Sports consists of 150 videos extracted from sports broadcasts of varying
resolution; it is trimmed to contain a single action in all frames. UCF101 is collected from
YouTube and has 101 action categories where 24 of them contain localization annotations,corresponding to 3,204 videos. All UCF101 videos contain exactly one action, most of them
(74.6%) are trimmed to fit the action. In contrast, the MSR-II Action dataset consists of
203 actions in 54 videos where each video has multiple actions of three classes. The actions
are performed in a crowded environment with ample background movement. The MSRII videos are relatively long, on average 763 frames, and the videos are untrimmed. Thus
actions are temporary unconstrained which adds temporal localization. Compared to thetrimmed UCF-Sports/UCF101 videos, the untrimmed nature of MSR-II makes this dataset
the most realistic and the most challenging.

6

VAN GEMERT, JAIN, GATI, SNOEK: ACTION LOCALIZATION PROPOSALS FROM DENSE TRAJECTORIESUCF sports

UCF 101

UCF-Sports: k vs time/accuracy per feature (150 actions)

0.30

MSR-II: k vs time/accuracy per feature (203 actions)
spat
hog
hof
traj
mbh

0.25
0.20

0.5

% Recall ( ≥ 0.5 overlap)% Recall ( ≥ 0.5 overlap)

0.25

0.4

0.15

0.20

0.3

0.10

0.15

k=5 k=10
0.2 k=3
15

MSR-II

UCF101: k vs time/accuracy per feature (3204 actions)
spat
hog
hof
traj
mbh

% Recall ( ≥ 0.5 overlap)

0.6

spat

hog

hof

20

25

30

k=20
traj

mbh

35

40

Average time per video (s)

k=30
45

0.05

k=3k=5

k=10

0.10
50

40

50

k=20
60

70

k=30
80

Average time per video (s)

90

0.00

k=3 k=5 k=10

120

140

k=20
160

180

k=30
200

Average time per video (s)220Figure 3: Evaluating APT properties. Analysis of computation time (x-axis) versus recall(y-axis) for the five base features: SPAT, HOG, HOF, TRAJ, MBH, for various values of k.
Similar marker color denote a similar value of k, while the marker shape connected withdashed lines represent a single feature. Note that MBH does not perform well on trimmed
UCF-Sports/UCF 101, while MBH is the best for untrimmed MSR-II. We draw 2 conclusions: i) in practice, computation time scales linearly in k; ii) No single feature will lead togood performance, heterogeneous features are key.
The quality of a proposal P with a ground truth tube G is evaluated with spatio-temporaltube overlap measured as the average “intersection-over-union" (IoU) score for 2D boxes for
all frames where there is either a ground truth box or a proposal box. More formally, for avideo V of F frames, a tube of bounding boxes is given by (B1 , B2 , ...BF ), where B f = 0,
/ if
there is no action i in frame f . The IoU score between G and P is
IoU(G, P) =G f ∩ Pf1,
|φ | f∑∈φ G f ∪ Pf

(1)

where φ is the set of frames where at least one of Pf , G f is not empty. A detection is correct
if the action is correctly predicted and the localization score is accurate, i.e., IoU(G, P) ≥ σ .
We set the threshold for the localization score to σ = 0.5 to evaluate recall, as commonly
done for object detection [1, 15, 25]. When comparing with other action localization methods, we also report on lower σ values if σ ≥ 0.5 is not reported.

4.2

Evaluating APT properties

The effect of the k-NN parameter. The k-parameter sets the number of spatio-temporalneighbors considered in the grouping. In Fig 3 we illustrate its effect on the computation
time and the recall accuracy. For practical values of k ≤ 30 the computation time is linearly
related to k. The recall accuracy for each single feature is not heavily influenced by the valueof k, from now on we use k = 10.
Which trajectory features to use. We plot recall accuracy for each of the five basefeatures: SPAT, HOG, HOF, TRAJ, MBH independently in Fig 3. Note the role switch of the
TRAJ and MBH features for trimmed UCF-Sports/UCF101 versus untrimmed MSR-II. The
TRAJ feature performs well on the trimmed UCF-Sports and UCF101, while TRAJ scores
badly on MSR-II. For MSR-II, the best single feature is MBH which performs sub-par on
UCF101/UCF-Sports. The untrimmed videos of MSR-II benefit from motion boundaries for
temporal segmentation while the majority of trimmed videos in UCF101 can focus on spatial
trajectories since temporal segmentation is not an issue in trimmed videos. We conclude

VAN GEMERT, JAIN, GATI, SNOEK: ACTION LOCALIZATION PROPOSALS FROM DENSE TRAJECTORIES

Effect of time scaling

0.9
0.8

0.9

Timescale=0
Timescale=1

Effect of feature normalization

Whitening Norm
Min-Max Norm

0.8

% Recall ( ≥ 0.5 overlap)

0.7% Recall ( ≥ 0.5 overlap)

0.7

7

0.6

0.6

0.5

0.5

0.4

0.4

0.3

0.3

0.2

0.2

0.1

0.1

0.0

0.0

UCF-Sports

UCF101MSR-II

UCF-Sports

UCF101MSR-II

Figure 4: Evaluating APT properties. Left: Effect of scaling the temporal-axis in APT.
Right: Effect of feature normalization when combining features. Note the conflict fortrimmed (UCF101) vs untrimmed (MSR-II) in both graphs.

Computation time

104

Figure 5: Evaluating APT computation
time on all videos of MSR-II. Note thelog scale on the y-axis. APT is typically faster than the pre-processing usedfor video segmentation (Xu & Corso [32])in the tubelets localisation proposals [9].
APT is an order of magnitude faster than
the trajectory clustering of Brox & Malik [2].

Seconds

103

102

101200Brox eccv'10
Xu cvpr'12
APT (ours)
400

600

800

1000

Number of frames

1200

1400

that no single feature is best, and heterogeneous features are essential. We use all feature
combinations for generating proposals.
The importance of the time scale. For trimmed videos the temporal axis is not important and we evaluate setting the time scale to 0 when computing the kNN. The results in Fig 4(left) show no effect for UCF-Sports. For UCF101 results improves significantly by ignoring
time, whereas for MSR-II ignoring the time scale reduces performance dramatically. Thisshows that temporal modeling is important for untrimmed videos, whereas time should be
ignored for trimmed videos. For APT we ignore the time scale for UCF-Sports and UCF101.
Feature normalization. The dense trajectory features ranges vary. Thus when similarities are combined by multiplication, their weight is not equal. We evaluate two featurenormalization schemes: i) min-max scaling of the similarities and ii) whitening the similarities in a video. The results in Fig 4 (right) show that UCF-Sports is agnostic to normalization.
UCF101 has a preference for whitening, whereas MSR-II has a preference for min-max scaling. This illustrates another difference between trimmed and untrimmed videos, and we use
whitening for UCF-Sports, UCF101 and min-max for MSR-II.
Success and failure cases. In Fig 1 and Fig 2, we show success and failure cases of
APT. When static actions are performed, our method has difficulties finding the action. On
the other hand, APT performs well even with severe camera-motion as in Fig 1 and large
scale changes as in Fig 2. Since APT is based on dense trajectories, which are meant foraction recognition, our proposals perform best when motion is present.

8

VAN GEMERT, JAIN, GATI, SNOEK: ACTION LOCALIZATION PROPOSALS FROM DENSE TRAJECTORIESUCF Sports

UCF 101

UCF-Sports: recall (%) per IoU threshold

MSR-II: recall (%) per IoU threshold

Brox eccv'10
Yu cvpr'15
APT (ours)

1.0

Recall (% ≥ IoU)

0.8

Recall (% ≥ IoU)

0.6

0.6

0.4

0.6

0.4

0.2

0.4

0.2

0.0
0.0

0.2

0.4

0.6

Intersection over Union (IoU)

0.8

1.0

UCF-Sports: Proposal quality per classBar-swing
Walk
Skateboard
Bench-swing
kick
Lift
Dive
Golf-swing
Run
Ride-horse
0.0

APT (ours)
Oneata eccv'14

0.2

0.4

0.6

% Recall (IoU ≥ 0.5)
Jain cvpr'14
Gkioxari cvpr'15

0.8

1.0

Brox eccv'10

0.0
0.0

SoccerJuggling
Fencing
RopeClimbing
IceDancing
WalkingWithDog
FloorGymnastics
BikingHorseRiding
TrampolineJumping
SkateBoarding
GolfSwing
SalsaSpin
DivingLongJump
TennisSwing
SkiingBasketballDunk
PoleVault
CliffDiving
VolleyballSpiking
CricketBowling
Surfing
SkijetBasketball
0.0

Brox eccv'10
Jain cvpr'14
Yu cvpr'15
APT (ours)

1.0

0.8

Recall (% ≥ IoU)

0.8

MSR-IIUCF 101: recall (%) per IoU threshold

Brox eccv'10
Gkioxari cvpr'15
Jain cvpr'14
Oneata eccv'14
APT (ours)

1.0

0.2

0.2

0.4

0.6

Intersection over Union (IoU)

0.8

1.0

0.0
0.0

0.2

UCF 101: Proposal quality per class

0.4

0.6

Intersection over Union (IoU)

0.8

1.0

MSR-II: Proposal quality per class
BoxingWavingClapping

APT (ours)
Brox eccv'10
0.2

0.4

0.6

% Recall (IoU ≥ 0.5)

0.8

APT (ours)
Yu cvpr'15
1.0

0.0

0.2

0.4

0.6

% Recall (IoU ≥ 0.5)

Jain cvpr'14
Brox eccv'10
0.8

1.0

Figure 6: Comparing action proposals. Top row: recall for a varying threshold σ . Bottom
row: recall per action category, where not all categories have proposals for σ = 0.5. For UCF
Sports, exception for Ride-Horse, we are competitive with others, and even outperforming
all others for Run. For UCF101 and MSR-II we outperform others on all actions.

4.3

Comparing action proposals

Speed. We evaluate computation speed on MSR-II which contains longer videos. Longer
videos have relatively less overhead and thus provide more accurate measurements than shortvideos. In Fig 5 we compare the speed of our proposals against flow clustering of Brox
& Malik [2] and the GB segmentation method by Xu & Corso [32]. GB is used as preprocessing in the spatio-temporal proposals by Jain et al. [9]. APT is typically faster thanpre-processing [32] and an order of magnitude faster than flow clustering [2].
Proposal quality. In Fig 6 we show the proposal quality for the three datasets, including
an analysis of recall per action category.
We first compare our method against an alternative trajectory clustering by Brox & Malik [2] on all three datasets. We used the author recommended settings, and fit tubes aroundthe output segment. As can be seen in Fig 6 this method [2] is not well-suited for action proposals as it offers a precise, high-quality object segmentation. The goals for object proposals,
however, are different: 1. a loose bounding box tube instead of a precise segmentation; 2.
multiple hypotheses instead of a single grouping. We conclude that the goals of video segmentation do not necessarily align well with the goals for action proposals. We detail acomparison with the state-of-the-art per dataset, next.
On UCF Sports we perform best for 9 out of 10 actions. The only exception is RideHorse, where only the fully supervised method of Gkioxari & Malik [7] gives excellent
results, suggesting that this class is hard for unsupervised methods based on motion. For allother actions our proposals are competitive or improve over the state-of-the-art [7, 9, 17].
For the motion-rich category Run we outperform all other methods, illustrating the powerof dense trajectories to capture motion proposals. For UCF101 and MSR-II we outperform
others on all categories. For UCF 101 we compare against the recent work of Yu & Yuan [33]
who only report recall for 0.1 and 0.05 IoU. We outperform this method with a large margin

VAN GEMERT, JAIN, GATI, SNOEK: ACTION LOCALIZATION PROPOSALS FROM DENSE TRAJECTORIES
ABO

MABO

Recall#Proposals

UCF Sports
Brox & Malik, ECCV 2010 [2]Jain et al., CVPR 2014 [9]Oneata et al., ECCV 2014 [17]Gkioxari & Malik, CVPR 2015 [7]APT (ours)

29.84
63.41
56.49
63.07
65.73

30.90
62.71
55.58
62.09
64.21

17.02
78.72
68.09
87.23
89.36

4
1,642
3,000
100
1,449

UCF 101
Brox & Malik, ECCV 2010 [2]APT (ours)

13.16
42.43

12.68
41.90

1.40
36.84

3
2,299

MSR-II
Brox & Malik, ECCV 2010 [2]Jain et al., CVPR 2014 [9]Yu & Yuan, CVPR 2015 [33]APT (ours)

2.28
34.88
n.a.
48.02

2.34
34.81
n.a.
47.87

0
2.96
0
44.33

6
4,218
37
6,706

9

Table 2: Comparing action proposals for commonly used metrics against
other methods, where n.a. denotes notreported values. ABO is the Average
Best Overlap, MABO the Mean ABOover all classes, Recall is measured at
an IoU overlap σ ≥ 0.5 and the number of proposals is averaged per video.
APT outperforms others with a modest
number of proposals.

and also report localization results for IoU ratios up to 1.0. The category-recall is not reported
in [33], thus we do not show per-category results for this method. For the untrimmed MSR-II
dataset we compare against [9, 33]. For more precise localization results on MSR-II with
IoU overlap scores of σ ≥ 0.2 we outperform both methods with a large margin (top right,
Fig 6). The results per category re-iterate this point, where [33] reports no proposals above
the IoU overlap threshold of σ ≥ 0.5.
In Table 2 we summarize proposal results with commonly used metrics. With a modest
number of proposals our method outperforms all others on ABO, MABO and Recall.

4.4

Classifying action proposals

For classification we re-use the improved dense trajectories [27], exploiting exactly the same
trajectory features as used for creating the action proposals. We compute a standard Fisher
vector [16, 18, 27] aggregated over the trajectories that form a proposal. We concatenate all
descriptors to a 426d vector, use PCA to half the dimensionality to 213d and fit 128 GMMs.
For training, we use a one-vs-rest SVM classifier with a Hellinger kernel.
In Fig 7 (left) we show the Area Under the ROC curve for a varying recall threshold σ on
UCF-Sports. The fully supervised method of Gkioxari & Malik [7] gives excellent results.
Their method, however, does not use spatio-temporal proposals, and needs to temporally
aggregate detection results for each class separately. This scales poorly to larger datasetswith more classes. Our results are competitive with the state of the art, while retaining fully
unsupervised action proposals.
For the large UCF 101 dataset we follow [33] and report mean average precision (mAP)over all 24 classes. In Fig 7 (middle) we show mAP scores for increasing IoU thresholdqualities and compare with the recent work of Yu & Yuan [33] that evaluated on this dataset.
They report mAP for only a single IoU threshold, which we outperform.
For MSR-II we follow standard practice and train on KTH [3, 33]. In Fig 7 (right) weshow mAP scores for a varying IoU threshold. To reduce clutter we only show the bestreported number on this dataset [33]. A comparison with the full state of the art is given in
Table 3, where an IoU threshold of 0.125 is common. We are best for Clap, and second forBox. On average we outperform all methods on MSR-II.

10

VAN GEMERT, JAIN, GATI, SNOEK: ACTION LOCALIZATION PROPOSALS FROM DENSE TRAJECTORIESUCF sports

UCF 101

0.6

0.6

0.5

0.5

0.4

MSR-II
0.8
0.7
0.6

0.4

0.2
0.1
0.0
0.1

Lan iccv'11
Wang eccv'14
Tian cvpr'13
Jain cvpr'14
Gkioxari cvpr'15
APT (ours)
0.2

mAP

mAP

AUC

0.5
0.3

0.3
0.2
0.1

0.3

0.4

IoU threshold

0.5

0.6

0.0
0.0

0.4
0.3
0.2

Yu cvpr'15
APT (ours)
0.1

0.2

0.3

0.4

IOU threshold

0.5

0.6

0.7

0.1
0.0

Yu cvpr'15
APT (ours)
0.1

0.2

0.3

0.4

IOU threshold

0.5

0.6

0.7

0.8

Figure 7: Classifying action proposals with a varying IoU threshold σ . Left: AUC on UCF
Sports, the fully supervised method of Gkioxari & Malik [7] is best, we are competitive withthe unsupervised state of the art. Middle: mAP for UCF 101, we outperform the recent
scores on this dataset by Yu & Yuan [33]. Right: mAP for MSR-II, where we outperformthe best results on this set by Yu & Yuan [33]. To reduce clutter for MSR-II we only plot thebest results, others are in Table 3.
Table 3: Classifying action proposals on MSR-II
for a threshold of 0.125.
Our average precision
score is best for Clap andrunner-up for Boxing. On
average we outperform allother methods.

5

MethodBoxing

Clap

Wave

mAP

Cao et al., CVPR 2010 [3]Tian et al., CVPR 2013 [22]Jain et al., CVPR 2014 [9]Yuan et al., TPAMI 2011 [34]Wang et al., ECCV 2014 [29]Yu & Yuan, CVPR 2015 [33]APT (ours)

17.48
38.86
46.00
64.90
41.70
67.40
67.02

13.16
23.91
31.41
43.10
50.20
46.60
78.40

26.71
44.70
85.79
64.90
80.90
69.90
74.14

19.12
35.82
54.40
55.30
57.60
61.30
73.19

Conclusion

We describe APT: an apt method for spatio-temporal proposals for action localization basedon the same dense trajectories as proved effective as a video representation for action classification. By reusing the trajectories, we preempt any pre-processing such as video segmentation as used by other proposal methods. Our method is efficient and outperforms the state of
the art on localization and classification on three datasets. Results show that APT is particularly effective for realistic untrimmed videos where temporal localization is essential. These
results are promising to facilitate action localization for large realistic video collections.

6

Acknowledgement

This research is supported by the STW STORY project and the Dutch national program
COMMIT.

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